{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llm-bootcamp-the-full-stack","slug":"llm-bootcamp-the-full-stack","name":"LLM Bootcamp - The Full Stack","type":"product","url":"https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/","page_url":"https://unfragile.ai/llm-bootcamp-the-full-stack","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-llm-bootcamp-the-full-stack__cap_0","uri":"capability://planning.reasoning.structured.llm.application.architecture.curriculum","name":"structured llm application architecture curriculum","description":"Teaches systematic decomposition of full-stack LLM systems into discrete architectural layers (data pipelines, model selection, prompt engineering, retrieval, evaluation). Uses case-study-driven pedagogy with real production patterns including RAG systems, fine-tuning workflows, and deployment strategies. Covers the complete lifecycle from prototyping to monitoring in production environments.","intents":["Learn how to architect production-grade LLM applications from first principles","Understand the full stack dependencies between data preparation, model choice, and inference optimization","Build mental models for when to use retrieval vs fine-tuning vs prompt engineering","Design evaluation frameworks for LLM outputs in real applications"],"best_for":["ML engineers transitioning from traditional ML to LLM-based systems","Full-stack developers building LLM products without prior deep learning experience","Technical founders prototyping LLM-powered MVPs who need architectural guidance","Teams evaluating whether to build vs integrate vs fine-tune LLM solutions"],"limitations":["Bootcamp format (typically 4-8 weeks) may not provide depth for specialized topics like constitutional AI or advanced RLHF","Curriculum snapshot from Spring 2023 — may not cover latest model releases (GPT-4, Claude 3, Llama 2 fine-tuning advances)","Hands-on labs require cloud compute credits (AWS/GCP) which add cost beyond tuition","No formal certification or credential upon completion — value is knowledge transfer only"],"requires":["Python 3.8+ (for lab notebooks and frameworks like LangChain, Hugging Face)","Basic ML/statistics background (understanding of loss functions, train/test splits)","Familiarity with APIs and REST concepts","Access to LLM APIs (OpenAI, Anthropic, or open-source model hosting)","GPU compute access for fine-tuning labs (T4/A100 recommended)"],"input_types":["Video lectures","Jupyter notebooks with executable code","Research papers and technical documentation","Real datasets for RAG and fine-tuning labs"],"output_types":["Trained mental models of LLM system design","Working code examples (Python, deployed to cloud)","Evaluation metrics and benchmarking frameworks","Architecture decision documents for LLM projects"],"categories":["planning-reasoning","education-curriculum"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_1","uri":"capability://memory.knowledge.hands.on.rag.system.design.and.implementation","name":"hands-on rag system design and implementation","description":"Teaches retrieval-augmented generation patterns including vector database selection, embedding model evaluation, prompt augmentation with retrieved context, and ranking strategies. Labs involve building end-to-end RAG pipelines using frameworks like LangChain, integrating with vector stores (Pinecone, Weaviate, Chroma), and evaluating retrieval quality with metrics like NDCG and MRR.","intents":["Build a RAG system that grounds LLM outputs in proprietary documents or knowledge bases","Choose between vector databases and embedding models for specific latency/accuracy trade-offs","Implement multi-stage retrieval (BM25 + semantic search) to improve recall","Evaluate whether RAG or fine-tuning is the right approach for a given use case"],"best_for":["Teams building question-answering systems over internal documentation","Developers implementing semantic search for large document collections","Startups needing to ground LLM outputs without fine-tuning costs","ML engineers evaluating vector database trade-offs (latency, cost, scalability)"],"limitations":["RAG quality heavily depends on embedding model choice — curriculum may not cover latest embedding models (e.g., BGE, E5) released post-Spring 2023","No coverage of advanced retrieval techniques like hypothetical document embeddings (HyDE) or query expansion beyond basic patterns","Vector database benchmarks change rapidly; curriculum examples may use outdated performance assumptions","Limited guidance on handling retrieval failures or out-of-distribution queries"],"requires":["Python 3.8+","Vector database (Pinecone, Weaviate, Chroma, or Milvus) with API access","Embedding API (OpenAI, Hugging Face, or local model)","Document corpus (minimum 100+ documents for meaningful retrieval evaluation)","LLM API for generation (OpenAI, Anthropic, or local)"],"input_types":["Unstructured text documents (PDFs, markdown, web pages)","Structured metadata (document titles, timestamps, categories)","User queries (natural language questions)"],"output_types":["Retrieved document chunks ranked by relevance","Augmented prompts with retrieved context","Generated answers grounded in retrieved documents","Retrieval quality metrics (precision@k, NDCG, MRR)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_2","uri":"capability://code.generation.editing.llm.fine.tuning.strategy.and.implementation","name":"llm fine-tuning strategy and implementation","description":"Covers when to fine-tune vs prompt-engineer vs use RAG, including cost-benefit analysis, data preparation workflows, and training on open-source models (Llama, Mistral) and commercial APIs (OpenAI fine-tuning). Labs involve preparing datasets, training on cloud GPUs, and evaluating fine-tuned models against baselines using metrics like BLEU, ROUGE, and task-specific accuracy.","intents":["Decide whether fine-tuning is cost-effective for your use case vs prompt engineering or RAG","Prepare and clean training data for LLM fine-tuning (handling imbalance, quality issues)","Train a fine-tuned model on open-source LLMs or via OpenAI's API","Evaluate fine-tuned models and measure improvement over base models"],"best_for":["Teams with domain-specific tasks (legal document analysis, medical coding) where fine-tuning ROI is high","Developers optimizing for latency or cost by using smaller fine-tuned models instead of large base models","Organizations with proprietary training data who want to avoid sending data to third-party APIs","ML engineers evaluating open-source model fine-tuning vs commercial API fine-tuning"],"limitations":["Fine-tuning economics have shifted post-Spring 2023 with cheaper APIs and better prompt engineering — curriculum may overstate fine-tuning ROI","No coverage of advanced techniques like LoRA or QLoRA for efficient fine-tuning on consumer GPUs","Limited guidance on data quality requirements (how much data is needed, what quality threshold)","Evaluation metrics (BLEU, ROUGE) are proxy metrics — curriculum may not emphasize human evaluation importance"],"requires":["Python 3.8+","Training dataset (minimum 100-1000 examples depending on task complexity)","GPU compute (A100 or V100 for full fine-tuning, or T4 for LoRA-based approaches)","LLM API key (OpenAI) or access to open-source model weights (Hugging Face)","Evaluation framework (e.g., HELM, LM Evaluation Harness)"],"input_types":["Labeled training examples (instruction-response pairs, classification labels)","Base model weights (from Hugging Face or OpenAI)","Validation dataset for hyperparameter tuning"],"output_types":["Fine-tuned model weights or API-hosted fine-tuned model","Training curves and loss metrics","Evaluation results (task-specific metrics, comparison to base model)","Cost analysis (compute cost vs performance gain)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_3","uri":"capability://data.processing.analysis.llm.evaluation.and.benchmarking.framework.design","name":"llm evaluation and benchmarking framework design","description":"Teaches systematic evaluation of LLM outputs using automated metrics (BLEU, ROUGE, METEOR, BERTScore), task-specific metrics (accuracy, F1, NDCG), and human evaluation protocols. Covers designing evaluation datasets, building evaluation pipelines, and interpreting results to guide model selection and fine-tuning decisions. Includes frameworks like HELM and LM Evaluation Harness.","intents":["Design evaluation datasets that capture real-world performance requirements for your LLM application","Choose appropriate metrics (automated vs human) for your task and budget constraints","Build evaluation pipelines that compare multiple models and configurations systematically","Interpret evaluation results to make model selection and fine-tuning decisions"],"best_for":["ML engineers building production LLM systems who need rigorous evaluation before deployment","Teams comparing multiple LLM providers or model sizes to optimize cost/performance","Researchers benchmarking new LLM techniques or architectures","Product managers deciding whether LLM quality is sufficient for production release"],"limitations":["Automated metrics (BLEU, ROUGE) are known to correlate poorly with human judgment for generation tasks — curriculum may overstate their reliability","Human evaluation is expensive and time-consuming; curriculum may not provide practical guidance on scaling human evaluation","Evaluation datasets become stale as models improve — curriculum doesn't address dataset versioning or drift","No coverage of adversarial evaluation or robustness testing (e.g., prompt injection, out-of-distribution inputs)"],"requires":["Python 3.8+","Evaluation dataset (gold-standard labels or human annotations)","LLM API access for inference (OpenAI, Anthropic, or local model)","Evaluation libraries (NLTK, BERTScore, HELM, LM Evaluation Harness)","Budget for human evaluation (if using crowdsourcing platforms like Mechanical Turk)"],"input_types":["LLM outputs (generated text, predictions)","Reference outputs (gold-standard answers for comparison)","Evaluation prompts or test cases","Human annotations (for human evaluation protocols)"],"output_types":["Automated metric scores (BLEU, ROUGE, BERTScore, task-specific metrics)","Human evaluation results (inter-annotator agreement, quality ratings)","Evaluation reports comparing models or configurations","Recommendations for model selection or fine-tuning"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_4","uri":"capability://text.generation.language.prompt.engineering.and.in.context.learning.optimization","name":"prompt engineering and in-context learning optimization","description":"Teaches systematic prompt design including chain-of-thought prompting, few-shot learning, prompt templates, and iterative refinement. Covers techniques like role-based prompting, structured output formatting, and prompt injection mitigation. Labs involve building prompt evaluation pipelines and comparing prompt variants using automated metrics and human feedback.","intents":["Design effective prompts that elicit high-quality outputs from LLMs without fine-tuning","Use few-shot examples to guide model behavior for specific tasks","Structure prompts for complex reasoning tasks (chain-of-thought, step-by-step)","Evaluate and iterate on prompts systematically rather than ad-hoc trial-and-error"],"best_for":["Product teams rapidly prototyping LLM features without ML infrastructure","Developers building LLM applications who want to avoid fine-tuning costs","Non-technical stakeholders (product managers, domain experts) who can contribute to prompt design","Teams optimizing LLM outputs for specific domains or use cases"],"limitations":["Prompt engineering is brittle — small changes in wording can significantly affect outputs, making results hard to reproduce","Techniques that work for one model (GPT-4) may not transfer to others (Claude, Llama) — curriculum may not address model-specific prompt patterns","No systematic framework for prompt optimization — curriculum teaches heuristics rather than principled approaches","Prompt injection vulnerabilities are mentioned but not deeply covered; curriculum may not prepare teams for adversarial use cases"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","Text editor or Jupyter notebook for prompt iteration","Evaluation dataset or test cases for measuring prompt effectiveness","Optional: prompt management tools (Prompt Flow, LangSmith) for tracking prompt versions"],"input_types":["Natural language task descriptions","Few-shot examples (input-output pairs)","Structured data to be processed (JSON, tables, documents)"],"output_types":["Optimized prompts (text templates with placeholders)","Prompt evaluation results (quality scores, comparison metrics)","Prompt guidelines and best practices for specific tasks"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_5","uri":"capability://automation.workflow.llm.deployment.and.serving.infrastructure","name":"llm deployment and serving infrastructure","description":"Covers deploying LLM applications to production including containerization (Docker), orchestration (Kubernetes), API serving frameworks (FastAPI, Flask), and monitoring. Teaches cost optimization strategies (batching, caching, model quantization), latency optimization (inference optimization, distillation), and reliability patterns (fallbacks, retry logic, circuit breakers). Labs involve deploying models to cloud platforms (AWS, GCP, Azure).","intents":["Deploy an LLM application to production with appropriate scaling and reliability","Optimize inference latency and cost for production workloads","Monitor LLM application health and performance in production","Handle failures gracefully (fallbacks to alternative models, retry logic)"],"best_for":["ML engineers building production LLM services with SLA requirements","DevOps teams deploying LLM applications at scale","Startups optimizing LLM inference costs to improve unit economics","Teams migrating from prototype to production LLM systems"],"limitations":["LLM serving landscape is rapidly evolving (vLLM, TensorRT-LLM, SGLang) — curriculum may use outdated serving frameworks","Cost optimization techniques (quantization, distillation) trade off quality for speed — curriculum may not provide clear guidance on acceptable trade-offs","Monitoring LLM applications is different from traditional ML (hallucinations, drift in output quality) — curriculum may not cover LLM-specific monitoring","Multi-model serving and routing (e.g., routing to different models based on input) is not deeply covered"],"requires":["Docker and container knowledge","Cloud platform account (AWS, GCP, or Azure) with compute credits","Python 3.8+ and web framework (FastAPI, Flask)","Optional: Kubernetes knowledge for orchestration","Optional: monitoring tools (Prometheus, Grafana, DataDog)"],"input_types":["LLM model weights or API endpoints","Application code (Python, FastAPI)","Configuration files (Docker, Kubernetes manifests)"],"output_types":["Containerized LLM application (Docker image)","Deployed service with REST API","Monitoring dashboards and alerts","Cost and latency metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_6","uri":"capability://planning.reasoning.llm.application.architecture.patterns.and.design.decisions","name":"llm application architecture patterns and design decisions","description":"Teaches architectural patterns for LLM applications including agent architectures, multi-step reasoning pipelines, tool-use integration, and state management. Covers design decisions like when to use agents vs pipelines, how to structure context windows, and managing dependencies between LLM calls. Uses frameworks like LangChain and AutoGPT as case studies.","intents":["Design the overall architecture for an LLM application (agent vs pipeline vs hybrid)","Structure multi-step reasoning workflows with error handling and fallbacks","Integrate external tools and APIs into LLM applications","Manage state and context across multiple LLM calls"],"best_for":["Architects designing complex LLM systems with multiple components","Teams building autonomous agents or multi-step reasoning applications","Developers integrating LLMs into existing software systems","Technical leads making architectural trade-offs (complexity vs capability)"],"limitations":["Agent architectures are still evolving — curriculum patterns may become outdated as new architectures emerge","No coverage of advanced agent techniques like tree-of-thought or graph-based reasoning","State management patterns are framework-specific (LangChain, AutoGPT) — curriculum may not generalize to other frameworks","Limited guidance on debugging and testing complex multi-step LLM workflows"],"requires":["Python 3.8+","LLM framework (LangChain, AutoGPT, or similar)","LLM API access (OpenAI, Anthropic, or local model)","Understanding of software architecture patterns (dependency injection, composition, etc.)"],"input_types":["Task descriptions and requirements","Available tools and APIs to integrate","Constraints (latency, cost, reliability)"],"output_types":["Architecture diagrams and design documents","Implementation code using LLM frameworks","Trade-off analysis (complexity vs capability, cost vs quality)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_7","uri":"capability://data.processing.analysis.data.preparation.and.curation.for.llm.tasks","name":"data preparation and curation for llm tasks","description":"Teaches data collection, cleaning, annotation, and augmentation strategies for LLM fine-tuning and evaluation. Covers handling data quality issues (duplicates, noise, bias), designing annotation guidelines, and using crowdsourcing platforms. Includes techniques like data augmentation, synthetic data generation, and active learning for efficient labeling.","intents":["Collect and prepare high-quality training data for LLM fine-tuning","Design annotation guidelines and manage crowdsourced labeling","Identify and mitigate data quality issues (duplicates, noise, bias)","Use data augmentation and synthetic data to increase training set size efficiently"],"best_for":["ML teams preparing datasets for fine-tuning or evaluation","Product managers managing data collection for LLM projects","Researchers studying data quality impact on LLM performance","Teams with limited labeled data who need efficient labeling strategies"],"limitations":["Data quality requirements vary by task — curriculum may not provide task-specific guidance","Crowdsourcing quality is variable and hard to control — curriculum may overstate crowdsourcing reliability","Synthetic data generation quality depends on the generator model — curriculum may not address synthetic data bias","Active learning strategies are computationally expensive — curriculum may not provide practical guidance on scaling"],"requires":["Raw data source (documents, user interactions, or domain-specific data)","Annotation platform (Mechanical Turk, Scale AI, Prodigy, or internal tool)","Python 3.8+ for data processing and cleaning","Budget for crowdsourced annotations (if using external labeling)"],"input_types":["Raw text documents, images, or structured data","Domain expertise or guidelines for annotation","Existing labeled data (for active learning or data augmentation)"],"output_types":["Cleaned and deduplicated dataset","Annotated examples with quality metrics","Data quality reports (coverage, bias analysis, inter-annotator agreement)","Augmented or synthetic training data"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_8","uri":"capability://planning.reasoning.model.selection.and.comparison.framework","name":"model selection and comparison framework","description":"Teaches systematic evaluation of LLM options (GPT-4, Claude, Llama, Mistral, etc.) based on task requirements, cost, latency, and capabilities. Covers building comparison matrices, benchmarking models on task-specific metrics, and making trade-off decisions. Includes frameworks for evaluating open-source vs commercial models and predicting model performance on new tasks.","intents":["Choose the right LLM for your application based on cost, latency, and quality requirements","Benchmark multiple models on your specific task to compare performance","Decide between open-source models (self-hosted) and commercial APIs (managed)","Predict model performance on new tasks without extensive benchmarking"],"best_for":["Technical leads evaluating LLM options for new projects","ML engineers optimizing model selection for cost and performance","Teams migrating between LLM providers or model versions","Startups making foundational decisions about LLM infrastructure"],"limitations":["Model landscape changes rapidly — curriculum comparisons may be outdated within months","Model performance varies significantly by task — curriculum benchmarks may not generalize to your specific use case","Cost and latency characteristics change with model updates — curriculum may not reflect current pricing or performance","No systematic framework for predicting model performance on new tasks — curriculum relies on empirical benchmarking"],"requires":["Access to multiple LLM APIs (OpenAI, Anthropic, Hugging Face, etc.)","Task-specific evaluation dataset","Budget for benchmarking multiple models","Evaluation framework (HELM, LM Evaluation Harness, or custom)"],"input_types":["Task description and requirements (latency, cost, quality targets)","Evaluation dataset","Model candidates (API endpoints or model weights)"],"output_types":["Model comparison matrix (cost, latency, quality metrics)","Benchmarking results on task-specific metrics","Recommendation for model selection with trade-off analysis","Cost-benefit analysis for different model choices"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-bootcamp-the-full-stack__cap_9","uri":"capability://safety.moderation.llm.safety.alignment.and.responsible.deployment","name":"llm safety, alignment, and responsible deployment","description":"Covers safety considerations for LLM applications including prompt injection mitigation, output filtering, bias detection, and responsible deployment practices. Teaches techniques like constitutional AI, RLHF for alignment, and red-teaming for identifying vulnerabilities. Includes frameworks for assessing and mitigating risks in production systems.","intents":["Identify and mitigate safety risks in LLM applications (prompt injection, jailbreaking, bias)","Implement output filtering and content moderation for production systems","Design evaluation frameworks for detecting bias and harmful outputs","Deploy LLM applications responsibly with appropriate safeguards"],"best_for":["Teams deploying LLM applications to production with safety requirements","Organizations handling sensitive data or high-risk use cases (healthcare, finance, legal)","Developers building customer-facing LLM products","Security teams assessing LLM-related risks"],"limitations":["Safety techniques are evolving rapidly — curriculum may not cover latest attack vectors or defenses","No systematic framework for assessing safety risks — curriculum teaches heuristics rather than principled approaches","Red-teaming is expensive and time-consuming — curriculum may not provide practical guidance on scaling","Bias detection is task-specific — curriculum may not generalize to all domains"],"requires":["Understanding of LLM vulnerabilities and attack vectors","Safety evaluation frameworks (e.g., HELM, LM Evaluation Harness)","Content moderation APIs (OpenAI Moderation, Perspective API, or custom)","Red-teaming resources (internal team or external consultants)"],"input_types":["LLM outputs (generated text)","User inputs (prompts, potentially adversarial)","Evaluation datasets for bias and safety testing"],"output_types":["Safety assessment reports","Filtered or moderated outputs","Bias metrics and mitigation strategies","Red-teaming results and vulnerability reports"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":20,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ (for lab notebooks and frameworks like LangChain, Hugging Face)","Basic ML/statistics background (understanding of loss functions, train/test splits)","Familiarity with APIs and REST concepts","Access to LLM APIs (OpenAI, Anthropic, or open-source model hosting)","GPU compute access for fine-tuning labs (T4/A100 recommended)","Python 3.8+","Vector database (Pinecone, Weaviate, Chroma, or Milvus) with API access","Embedding API (OpenAI, Hugging Face, or local model)","Document corpus (minimum 100+ documents for meaningful retrieval evaluation)","LLM API for generation (OpenAI, Anthropic, or local)"],"failure_modes":["Bootcamp format (typically 4-8 weeks) may not provide depth for specialized topics like constitutional AI or advanced RLHF","Curriculum snapshot from Spring 2023 — may not cover latest model releases (GPT-4, Claude 3, Llama 2 fine-tuning advances)","Hands-on labs require cloud compute credits (AWS/GCP) which add cost beyond tuition","No formal certification or credential upon completion — value is knowledge transfer only","RAG quality heavily depends on embedding model choice — curriculum may not cover latest embedding models (e.g., BGE, E5) released post-Spring 2023","No coverage of advanced retrieval techniques like hypothetical document embeddings (HyDE) or query expansion beyond basic patterns","Vector database benchmarks change rapidly; curriculum examples may use outdated performance assumptions","Limited guidance on handling retrieval failures or out-of-distribution queries","Fine-tuning economics have shifted post-Spring 2023 with cheaper APIs and better prompt engineering — curriculum may overstate fine-tuning ROI","No coverage of advanced techniques like LoRA or QLoRA for efficient fine-tuning on consumer GPUs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:30.220Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-bootcamp-the-full-stack","compare_url":"https://unfragile.ai/compare?artifact=llm-bootcamp-the-full-stack"}},"signature":"c8CjjE1HnvOC82+YVE2yW1RF8yCcd7BOkORFwe7J3mzODrvQ8LpPIMo1AUWGGg7lrkcAb0E9TpPmqhOBXwVwAQ==","signedAt":"2026-06-20T19:55:07.545Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-bootcamp-the-full-stack","artifact":"https://unfragile.ai/llm-bootcamp-the-full-stack","verify":"https://unfragile.ai/api/v1/verify?slug=llm-bootcamp-the-full-stack","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"}}