{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-nirdiamant--prompt_engineering","slug":"nirdiamant--prompt_engineering","name":"Prompt_Engineering","type":"repo","url":"https://github.com/NirDiamant/Prompt_Engineering","page_url":"https://unfragile.ai/nirdiamant--prompt_engineering","categories":["prompt-engineering"],"tags":["ai","chatgpt","claude","genai","langchain","llm","llms","openai","prompt-engineering","prompting","python","tutorials"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-nirdiamant--prompt_engineering__cap_0","uri":"capability://text.generation.language.zero.shot.prompting.with.structured.templates","name":"zero-shot prompting with structured templates","description":"Teaches and implements zero-shot prompting by providing Jupyter notebook tutorials that demonstrate how to craft single-turn prompts without examples, using clear instruction structures and role definitions. The implementation uses OpenAI and Claude APIs with templated prompt patterns that guide LLMs to perform tasks based solely on task description and context, without requiring few-shot examples or chain-of-thought reasoning.","intents":["I want to understand how to write effective prompts without providing examples to the model","I need to quickly test if an LLM can solve a task with just a clear instruction","I want to learn the foundational patterns for prompt design before moving to advanced techniques"],"best_for":["developers new to LLM interaction","teams prototyping quick LLM integrations","educators teaching prompt engineering fundamentals"],"limitations":["Zero-shot approach may fail on complex reasoning tasks requiring step-by-step thinking","No built-in evaluation metrics to measure prompt effectiveness","Limited guidance on when zero-shot is insufficient vs when few-shot is needed"],"requires":["Python 3.8+","Jupyter Notebook or JupyterLab","OpenAI API key or Anthropic API key","Basic understanding of LLM APIs"],"input_types":["text (task description)","text (context/background information)"],"output_types":["text (LLM response)","structured text (formatted output based on prompt instructions)"],"categories":["text-generation-language","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_1","uri":"capability://text.generation.language.few.shot.learning.with.in.context.examples","name":"few-shot learning with in-context examples","description":"Implements few-shot prompting by providing Jupyter tutorials that demonstrate how to include 2-5 labeled examples in prompts to guide LLM behavior through demonstration rather than explicit instruction. The approach uses OpenAI/Claude APIs with structured example formatting, showing how to select representative examples, format them consistently, and measure their impact on model output quality and consistency.","intents":["I want to improve model accuracy by showing examples of desired behavior instead of just describing it","I need to teach the model a specific output format or style through demonstration","I want to understand how many examples are needed before diminishing returns set in"],"best_for":["developers building classification or extraction tasks","teams needing consistent output formatting","practitioners optimizing token usage vs accuracy tradeoffs"],"limitations":["Few-shot examples consume tokens, increasing API costs and latency","Model performance is sensitive to example selection and ordering — no automated optimization provided","No guidance on handling domain-specific or rare cases where good examples are hard to find"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Curated dataset of 2-5 representative examples"],"input_types":["text (input examples)","text (output examples)","text (new query to apply examples to)"],"output_types":["text (LLM response following example pattern)","structured data (formatted output matching example structure)"],"categories":["text-generation-language","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_10","uri":"capability://safety.moderation.negative.prompting.and.exclusion.based.guidance","name":"negative prompting and exclusion-based guidance","description":"Teaches negative prompting through Jupyter notebooks that demonstrate how to explicitly specify what the model should NOT do or produce, improving output quality by excluding unwanted behaviors. The approach uses OpenAI/Claude APIs with patterns like 'Do not include X' or 'Avoid Y' to guide models away from common failure modes, hallucinations, or undesired output characteristics. Includes techniques for identifying effective negative constraints.","intents":["I want to prevent the model from producing specific unwanted outputs or behaviors","I need to exclude hallucinations or off-topic content from responses","I want to understand how negative instructions complement positive instructions"],"best_for":["developers building systems sensitive to specific failure modes","teams preventing hallucinations or factual errors in outputs","practitioners refining prompts by learning from failure cases"],"limitations":["Negative instructions are less effective than positive guidance — models may still produce excluded content","Over-specifying exclusions can make prompts verbose and confusing","Effectiveness varies by model and task — requires testing per use case"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Understanding of common failure modes for the task"],"input_types":["text (task description)","list of exclusions (what NOT to do/produce)"],"output_types":["text (output without excluded content)","metrics (reduction in unwanted outputs)"],"categories":["safety-moderation","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_11","uri":"capability://data.processing.analysis.prompt.formatting.and.structured.output.generation","name":"prompt formatting and structured output generation","description":"Implements prompt formatting through Jupyter notebooks that teach how to structure prompts and specify output formats (JSON, markdown, tables, code) to ensure consistent, parseable results. The approach uses OpenAI/Claude APIs with explicit format directives and examples to guide models toward structured outputs, enabling downstream processing and integration with other systems. Includes validation patterns to verify output format compliance.","intents":["I want the model to produce outputs in a specific format (JSON, markdown, code) that I can parse","I need consistent structure across multiple outputs for downstream processing","I want to integrate LLM outputs with other systems that expect specific formats"],"best_for":["developers building LLM pipelines with downstream processing","teams needing structured data extraction from unstructured text","practitioners automating workflows that depend on consistent output formats"],"limitations":["Models may not always follow format specifications — requires validation and error handling","Specifying complex formats increases prompt length and token consumption","Format compliance varies by model — some models are better at following format directives"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","JSON schema or format specification library (optional)"],"input_types":["text (task description)","format specification (JSON schema, markdown template, etc.)","examples (sample outputs in desired format)"],"output_types":["structured data (JSON, markdown, code, tables)","validation results (format compliance check)"],"categories":["data-processing-analysis","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_12","uri":"capability://text.generation.language.multilingual.prompting.and.cross.language.reasoning","name":"multilingual prompting and cross-language reasoning","description":"Teaches multilingual prompting through Jupyter notebooks that demonstrate how to craft prompts for non-English languages and handle cross-language tasks (translation, multilingual reasoning, code-switching). The approach uses OpenAI/Claude APIs to show language-specific prompt patterns, handling of character encodings, and techniques for maintaining consistency across languages. Includes guidance on when to use native language vs English for better model performance.","intents":["I want to use LLMs effectively in languages other than English","I need to handle translation or multilingual reasoning tasks","I want to understand how language choice affects model performance and output quality"],"best_for":["developers building global applications supporting multiple languages","teams working with non-English content or users","practitioners optimizing prompts for specific languages"],"limitations":["Model performance varies significantly across languages — English-centric models perform worse in other languages","Character encoding and special characters can cause issues — requires careful handling","No guidance on optimal language choice for specific tasks (native vs English)"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Understanding of target language(s) and cultural context"],"input_types":["text (prompt in target language or multiple languages)","text (content in non-English language)"],"output_types":["text (response in target language)","text (translated or multilingual output)"],"categories":["text-generation-language","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_13","uri":"capability://safety.moderation.ethical.prompt.engineering.and.bias.mitigation","name":"ethical prompt engineering and bias mitigation","description":"Implements ethical prompting through Jupyter notebooks that teach how to design prompts that reduce bias, avoid harmful outputs, and align with ethical principles. The approach uses OpenAI/Claude APIs to demonstrate bias detection in prompts, techniques for neutral language, and methods for evaluating fairness and safety in outputs. Includes patterns for responsible AI practices in prompt design.","intents":["I want to identify and reduce bias in my prompts and model outputs","I need to ensure my prompts don't produce harmful or discriminatory content","I want to understand ethical considerations in prompt design and LLM use"],"best_for":["developers building systems for sensitive domains (hiring, lending, healthcare)","teams committed to responsible AI practices","practitioners learning to recognize and mitigate bias"],"limitations":["Bias detection is subjective and context-dependent — no universal definition","Removing bias from prompts doesn't guarantee unbiased outputs — model training matters","Ethical considerations vary by domain and culture — no one-size-fits-all approach"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Domain knowledge and ethical framework for the application"],"input_types":["text (prompt)","text (model output)"],"output_types":["text (bias-mitigated prompt)","metrics (bias/fairness scores)","analysis (bias detection results)"],"categories":["safety-moderation","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_14","uri":"capability://safety.moderation.prompt.security.and.safety.guardrails","name":"prompt security and safety guardrails","description":"Teaches prompt security through Jupyter notebooks that demonstrate how to design prompts resistant to adversarial attacks, prompt injection, and jailbreaking attempts. The approach uses OpenAI/Claude APIs to show common attack patterns, defensive prompt structures, and validation techniques to prevent misuse. Includes patterns for input sanitization, output validation, and detecting suspicious requests.","intents":["I want to protect my LLM application from prompt injection and jailbreaking attacks","I need to validate user inputs before sending them to the model","I want to understand security considerations in prompt design"],"best_for":["developers building production LLM systems exposed to untrusted inputs","teams deploying LLMs in security-sensitive contexts","practitioners learning to recognize and defend against prompt attacks"],"limitations":["No foolproof defense against all prompt injection attacks — requires defense-in-depth","Security measures may reduce model flexibility or user experience","Attack techniques evolve faster than defenses — requires continuous monitoring"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Understanding of security principles and threat models"],"input_types":["text (user input)","text (system prompt)"],"output_types":["text (sanitized input)","boolean (safety check result)","text (model output with validation)"],"categories":["safety-moderation","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_15","uri":"capability://data.processing.analysis.evaluating.prompt.effectiveness.with.metrics.and.benchmarks","name":"evaluating prompt effectiveness with metrics and benchmarks","description":"Implements prompt evaluation through Jupyter notebooks that teach how to measure prompt quality using metrics (accuracy, consistency, relevance), benchmarks, and test datasets. The approach uses OpenAI/Claude APIs to generate outputs, compare against ground truth or quality criteria, and quantify improvements. Includes techniques for designing evaluation frameworks and interpreting results across different models and tasks.","intents":["I want to measure whether my prompt improvements actually increase output quality","I need to compare different prompts objectively using metrics","I want to establish baselines and track prompt performance over time"],"best_for":["developers building production LLM systems requiring measurable quality","teams optimizing prompts with data-driven approaches","practitioners evaluating prompt effectiveness across models"],"limitations":["Evaluation metrics are task-specific — no universal metric works for all prompts","Ground truth may not exist for subjective tasks (creativity, style)","Evaluation requires test datasets which may be expensive to create"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Test dataset with expected outputs or quality criteria"],"input_types":["text (prompt)","list of test cases (input/expected output pairs)","list of metrics (accuracy, consistency, relevance, etc.)"],"output_types":["metrics (quality scores)","comparison data (prompt A vs B performance)","analysis (which metrics improved/degraded)"],"categories":["data-processing-analysis","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_16","uri":"capability://tool.use.integration.langchain.integration.for.prompt.orchestration","name":"langchain integration for prompt orchestration","description":"Teaches LangChain integration through Jupyter notebooks that demonstrate how to use LangChain's prompt templates, chains, and agents to orchestrate complex prompting workflows. The approach uses LangChain abstractions (PromptTemplate, LLMChain, agents) with OpenAI/Claude APIs to simplify multi-step prompting, variable substitution, and output parsing. Includes patterns for building reusable prompt components and managing state across chain steps.","intents":["I want to use LangChain to simplify complex prompting workflows","I need to build reusable prompt templates with variable substitution","I want to orchestrate multi-step chains with LangChain abstractions"],"best_for":["developers building complex LLM applications with LangChain","teams using LangChain for production systems","practitioners learning LangChain patterns and best practices"],"limitations":["LangChain abstractions add complexity and learning curve","Performance overhead from abstraction layers — not suitable for latency-critical applications","LangChain API changes may break existing code — requires version management"],"requires":["Python 3.8+","Jupyter Notebook","LangChain library (pip install langchain)","OpenAI API key or Anthropic API key"],"input_types":["text (prompt template with variables)","dict (variable values for substitution)"],"output_types":["text (LLM output)","structured data (parsed output from chain)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_17","uri":"capability://tool.use.integration.openai.api.integration.patterns.and.best.practices","name":"openai api integration patterns and best practices","description":"Implements OpenAI API integration through Jupyter notebooks that teach how to use OpenAI's API effectively for prompting, including authentication, model selection, parameter tuning (temperature, max_tokens), and error handling. The approach uses the OpenAI Python client library with patterns for managing API keys, handling rate limits, and optimizing costs. Includes best practices for production deployments.","intents":["I want to understand how to use the OpenAI API effectively for my prompting tasks","I need to optimize API usage for cost and performance","I want to learn best practices for production OpenAI integrations"],"best_for":["developers building applications with OpenAI API","teams optimizing OpenAI costs and performance","practitioners learning OpenAI API patterns and best practices"],"limitations":["OpenAI API pricing can be expensive at scale — requires cost monitoring","Rate limits may throttle requests — requires backoff and retry logic","API changes or deprecations may break existing code — requires version management"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI Python library (pip install openai)","OpenAI API key with appropriate permissions"],"input_types":["text (prompt)","dict (API parameters: temperature, max_tokens, etc.)"],"output_types":["text (model response)","dict (API metadata: tokens used, finish reason, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_2","uri":"capability://planning.reasoning.chain.of.thought.reasoning.decomposition","name":"chain-of-thought reasoning decomposition","description":"Teaches chain-of-thought (CoT) prompting through Jupyter notebooks that demonstrate how to structure prompts to make LLMs verbalize intermediate reasoning steps before producing final answers. The implementation uses explicit prompt patterns like 'Let's think step by step' and shows how to parse multi-step reasoning outputs, enabling better performance on complex reasoning, math, and logic tasks by forcing the model to show its work.","intents":["I want the model to show its reasoning process so I can debug incorrect answers","I need better accuracy on math, logic, or multi-step reasoning problems","I want to understand how to structure prompts that encourage step-by-step thinking"],"best_for":["developers building reasoning-heavy applications (math solvers, logic engines)","teams debugging LLM errors by inspecting intermediate steps","educators teaching how LLMs approach complex problems"],"limitations":["CoT increases token consumption by 2-5x due to verbose reasoning output","Model may produce plausible-sounding but incorrect intermediate steps","No automated verification that reasoning is logically sound — requires manual inspection"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Understanding of the problem domain to validate reasoning steps"],"input_types":["text (complex problem statement)","text (optional: examples of step-by-step reasoning)"],"output_types":["text (multi-step reasoning with intermediate conclusions)","text (final answer with full derivation)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_3","uri":"capability://planning.reasoning.self.consistency.voting.across.multiple.reasoning.paths","name":"self-consistency voting across multiple reasoning paths","description":"Implements self-consistency prompting through Jupyter notebooks that demonstrate generating multiple independent reasoning chains for the same problem and selecting the most common final answer via majority voting. The approach uses OpenAI/Claude APIs to generate N diverse CoT outputs, then aggregates them to improve accuracy beyond single-chain reasoning, particularly effective for math and logic problems where multiple valid reasoning paths exist.","intents":["I want to improve accuracy on reasoning tasks by sampling multiple solution paths","I need to reduce the impact of single reasoning errors by using ensemble-like voting","I want to understand how to aggregate multiple LLM outputs into a consensus answer"],"best_for":["developers building high-stakes reasoning applications (math tutors, logic solvers)","teams with budget for multiple API calls per query","practitioners optimizing for accuracy over latency"],"limitations":["Requires N API calls per query (typically 3-5), multiplying costs and latency by N","Voting mechanism assumes majority is correct — fails when most paths are wrong","No built-in handling of ties or near-ties in voting results"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Budget for 3-5x API call volume"],"input_types":["text (problem statement)","integer (number of reasoning paths to generate, typically 3-5)"],"output_types":["text (multiple reasoning chains)","text (consensus answer via majority voting)","integer (vote count for winning answer)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_4","uri":"capability://text.generation.language.role.based.prompt.engineering.with.persona.injection","name":"role-based prompt engineering with persona injection","description":"Teaches role-based prompting through Jupyter notebooks that demonstrate how to inject personas or expert roles into prompts to guide LLM behavior and output style. The implementation uses patterns like 'You are a senior software architect' or 'Act as a data scientist' to prime the model toward specific expertise levels, communication styles, and domain knowledge, improving output relevance and quality for specialized tasks.","intents":["I want the model to respond with expertise appropriate to a specific role (architect, analyst, etc.)","I need to control the communication style and formality level of model outputs","I want to leverage domain-specific knowledge by priming the model with a relevant persona"],"best_for":["developers building specialized assistants (code reviewers, data analysts, content creators)","teams needing consistent tone and expertise level across outputs","practitioners experimenting with prompt variations for the same task"],"limitations":["Role effectiveness varies by model — some models respond strongly to personas, others ignore them","No guarantee that injected role actually changes behavior — requires testing per use case","Combining multiple roles or conflicting personas can produce unpredictable results"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Understanding of target role's expertise and communication style"],"input_types":["text (role/persona description)","text (task or query)"],"output_types":["text (response in the voice/style of the specified role)","structured text (domain-specific output format)"],"categories":["text-generation-language","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_5","uri":"capability://planning.reasoning.task.decomposition.and.prompt.chaining","name":"task decomposition and prompt chaining","description":"Implements task decomposition through Jupyter notebooks that teach how to break complex problems into sequential sub-tasks, each with its own prompt, and chain the outputs together. The approach uses OpenAI/Claude APIs to execute multi-step workflows where output from one prompt feeds into the next, enabling complex reasoning, content generation, and problem-solving by reducing each step's complexity.","intents":["I want to solve complex problems by breaking them into smaller, manageable steps","I need to build multi-step workflows where each step refines or builds on previous outputs","I want to understand how to structure prompts so outputs from one step work as inputs to the next"],"best_for":["developers building complex LLM applications (research assistants, code generators, content pipelines)","teams automating multi-step processes (analysis → synthesis → formatting)","practitioners optimizing for accuracy by isolating concerns into separate prompts"],"limitations":["Errors in early steps propagate through the chain — no built-in error recovery","Requires careful output formatting to ensure downstream prompts can parse upstream results","Token consumption scales with number of steps — no optimization for reducing intermediate context"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","LangChain or similar orchestration library (optional but recommended)"],"input_types":["text (initial problem or query)","list of prompts (one per decomposed sub-task)"],"output_types":["text (final output after all chain steps)","list of intermediate outputs (one per step)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_6","uri":"capability://text.generation.language.instruction.engineering.and.constraint.based.generation","name":"instruction engineering and constraint-based generation","description":"Teaches instruction engineering through Jupyter notebooks that demonstrate how to write precise, unambiguous instructions that guide LLM behavior toward specific outputs. The implementation covers constraint specification (output format, length, style), negative instructions (what NOT to do), and structured directives that reduce ambiguity and improve output consistency. Uses OpenAI/Claude APIs with examples of instruction clarity improvements.","intents":["I want to write clearer instructions that produce more consistent and correct outputs","I need to specify constraints like output format, length, or style requirements","I want to understand how to use negative instructions to exclude unwanted behaviors"],"best_for":["developers building production LLM systems requiring consistent outputs","teams optimizing prompts for specific output formats (JSON, markdown, structured text)","practitioners refining prompts through iterative instruction clarity improvements"],"limitations":["Instruction clarity is subjective — what's clear to humans may be ambiguous to models","Over-constraining instructions can reduce output quality or creativity","No automated validation that outputs actually follow specified constraints"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Understanding of target output format and constraints"],"input_types":["text (task description)","text (constraints: format, length, style, exclusions)"],"output_types":["text (constrained output following specified format)","structured data (JSON, markdown, or other specified format)"],"categories":["text-generation-language","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_7","uri":"capability://data.processing.analysis.prompt.optimization.through.iterative.refinement","name":"prompt optimization through iterative refinement","description":"Implements prompt optimization through Jupyter notebooks that teach systematic approaches to improving prompts through iteration, testing, and measurement. The approach uses OpenAI/Claude APIs to generate outputs, measure quality against criteria (accuracy, format compliance, style), and iteratively refine prompts based on results. Includes techniques for A/B testing prompt variations and identifying which changes improve performance.","intents":["I want to systematically improve a prompt by testing variations and measuring results","I need to understand which prompt changes actually improve output quality","I want to optimize prompts for specific metrics (accuracy, speed, cost, style consistency)"],"best_for":["developers building production LLM systems requiring high-quality outputs","teams with test datasets for measuring prompt effectiveness","practitioners optimizing prompts for specific use cases or domains"],"limitations":["Optimization requires multiple API calls per iteration — can be expensive at scale","No automated optimization algorithm — requires manual hypothesis generation and testing","Results may not generalize across different models or model versions"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Test dataset with expected outputs or quality criteria"],"input_types":["text (initial prompt)","list of test cases (input/expected output pairs)","list of quality metrics (accuracy, format, style, etc.)"],"output_types":["text (optimized prompt)","metrics (performance scores for each iteration)","comparison data (before/after quality improvements)"],"categories":["data-processing-analysis","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_8","uri":"capability://text.generation.language.handling.ambiguity.and.clarity.in.prompts","name":"handling ambiguity and clarity in prompts","description":"Teaches ambiguity handling through Jupyter notebooks that demonstrate how to identify and resolve ambiguous language in prompts that could lead to multiple interpretations. The approach uses OpenAI/Claude APIs to show how clarifying questions, context provision, and explicit scope definition reduce ambiguity and improve output consistency. Includes patterns for detecting when a prompt is ambiguous and techniques for making it more precise.","intents":["I want to identify when my prompts are ambiguous and causing inconsistent outputs","I need to add context or clarification to reduce multiple interpretations","I want to understand how to scope prompts precisely to avoid off-topic responses"],"best_for":["developers debugging inconsistent LLM outputs","teams building systems requiring deterministic behavior","practitioners learning to write clearer prompts through examples"],"limitations":["Ambiguity detection is subjective — no automated tool to identify all ambiguities","Adding context to reduce ambiguity increases token consumption","Some ambiguities are inherent to the problem domain and can't be fully resolved"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Domain knowledge to identify relevant ambiguities"],"input_types":["text (ambiguous prompt)","text (context or clarification)"],"output_types":["text (clarified prompt)","text (LLM output with reduced ambiguity)","list of potential interpretations (before clarification)"],"categories":["text-generation-language","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--prompt_engineering__cap_9","uri":"capability://data.processing.analysis.prompt.length.and.complexity.management","name":"prompt length and complexity management","description":"Implements prompt length optimization through Jupyter notebooks that teach how to balance prompt detail with token efficiency and model performance. The approach uses OpenAI/Claude APIs to demonstrate how longer prompts with more context improve accuracy but increase costs and latency, while shorter prompts reduce costs but may lose important context. Includes techniques for identifying essential vs redundant information and strategies for compression.","intents":["I want to reduce token consumption without sacrificing output quality","I need to understand the tradeoff between prompt detail and API costs","I want to identify which parts of my prompt are essential vs redundant"],"best_for":["developers optimizing LLM costs in production systems","teams with latency constraints requiring faster responses","practitioners balancing accuracy vs efficiency"],"limitations":["Removing context to reduce length may degrade output quality — requires testing","Optimal length varies by task and model — no universal formula","Compression techniques (summarization, templating) require manual effort"],"requires":["Python 3.8+","Jupyter Notebook","OpenAI API key or Anthropic API key","Token counting library (e.g., tiktoken)"],"input_types":["text (original prompt)","integer (target token budget)","metrics (quality thresholds)"],"output_types":["text (optimized prompt)","integer (token count before/after)","metrics (quality comparison)"],"categories":["data-processing-analysis","prompt-design"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Jupyter Notebook or JupyterLab","OpenAI API key or Anthropic API key","Basic understanding of LLM APIs","Jupyter Notebook","Curated dataset of 2-5 representative examples","Understanding of common failure modes for the task","JSON schema or format specification library (optional)","Understanding of target language(s) and cultural context","Domain knowledge and ethical framework for the application"],"failure_modes":["Zero-shot approach may fail on complex reasoning tasks requiring step-by-step thinking","No built-in evaluation metrics to measure prompt effectiveness","Limited guidance on when zero-shot is insufficient vs when few-shot is needed","Few-shot examples consume tokens, increasing API costs and latency","Model performance is sensitive to example selection and ordering — no automated optimization provided","No guidance on handling domain-specific or rare cases where good examples are hard to find","Negative instructions are less effective than positive guidance — models may still produce excluded content","Over-specifying exclusions can make prompts verbose and confusing","Effectiveness varies by model and task — requires testing per use case","Models may not always follow format specifications — requires validation and error handling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6391178930890578,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:59:50.673Z","last_commit":"2026-04-15T15:30:36Z"},"community":{"stars":7466,"forks":968,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nirdiamant--prompt_engineering","compare_url":"https://unfragile.ai/compare?artifact=nirdiamant--prompt_engineering"}},"signature":"e0t5YsE6Ofprjtnd9/8kNNdnjuGveZ9Oc1plkM9gbYNZYn78iPORRog4AG88YRz/HypoGcpoAFON2+7RyvK/AA==","signedAt":"2026-06-21T09:17:21.686Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nirdiamant--prompt_engineering","artifact":"https://unfragile.ai/nirdiamant--prompt_engineering","verify":"https://unfragile.ai/api/v1/verify?slug=nirdiamant--prompt_engineering","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}