Concept library
446 concepts across 8 domains and 36 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
06
Applied LLM Engineering
Building things people use: retrieval, prompts, agents and production architecture.
4tracks
33concepts
193cards
4.5hreading
Retrieval & RAG Vector stores, hybrid retrieval and reranking, and when to retrieve instead of fine-tune. 10 concepts · 74 cards
- 01 Chunking Strategies for Retrieval Why the unit you index decides the ceiling on retrieval quality, how fixed, recursive, semantic and contextual chunking differ, and what each one loses.
- 02 Fine-tuning vs RAG When to teach the model new behaviour vs when to retrieve fresh context at runtime.
- 03 Hybrid Retrieval - BM25 + Vector + Reranking Why pure vector search misses exact-match queries, how RRF combines lexical and semantic results, and where a cross-encoder reranker buys back the precision you lost.
- 04 Query Transformation for Retrieval Why the user's question is often a bad search query, and how rewriting, decomposition, multi-query fan-out and HyDE close the gap between how people ask and how documents are written.
- 05 RAG Evaluation and Groundedness How to separate retrieval failures from generation failures, which metrics actually diagnose each stage, and why groundedness is measurable while helpfulness mostly is not.
- 06 Reranking and Cross-Encoders Why a second-stage model that reads the query and document together fixes most retrieval failures, what it costs in latency, and how to size the candidate set.
- 07 Retrieval Augmented Generation The end-to-end RAG pipeline from chunking through retrieval, reranking, and grounded generation.
- 08 Vector Databases Compared - pgvector, Qdrant, Milvus, Weaviate, LanceDB A practitioner's guide to picking a vector store, weighing index trade-offs against the operational cost of running yet another database alongside your primary store.
- 09 ANN Indexes: HNSW, IVF and PQ How approximate nearest neighbour indexes trade recall for latency and memory, what HNSW, IVF-PQ, ScaNN and DiskANN each optimise for, and why recall is a knob rather than a property.
- 10 Late Interaction and Multi-Vector Retrieval How ColBERT-style models keep one vector per token instead of one per document, why MaxSim recovers most cross-encoder quality at index-time cost, and what the storage bill looks like.
Prompt Engineering In-context learning, chain of thought, structured output, compression and injection-aware design. 6 concepts · 34 cards
- 01 Chain of Thought Prompting Why telling the model to think step by step radically improves reasoning, and when it actively hurts.
- 02 Few-Shot and In-Context Learning Learning a task from a handful of worked examples placed in the prompt, with no weight updates, and the surprising evidence about what those examples actually teach.
- 03 Injection-Aware Prompt Design How to structure prompts that consume untrusted input so injection is harder, and why prompt design alone can never make an LLM injection-proof.
- 04 Prompt Chaining and Task Decomposition Splitting a hard task into a pipeline of simpler, individually-checkable prompts so each step can be validated, routed, and debugged on its own.
- 05 Structured Output Coercion How to coax reliable JSON, XML, and tabular output from a model using prompting alone, and why that gives you no hard guarantee the way constrained decoding does.
- 06 Prompt Compression Cutting prompt tokens while holding task performance, via perplexity-based token dropping (LLMLingua) or learned gist tokens, and when prompt caching beats both.
Agents & Tool Use Function calling, ReAct loops, MCP, agent memory architectures and evaluation harnesses. 10 concepts · 51 cards
- 01 Agent Frameworks Compared LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK solve different problems; the harder question is whether you need a framework at all.
- 02 Model Context Protocol (MCP) The open standard that replaces bespoke per-tool integrations with one protocol, so any compliant client can talk to any compliant server.
- 03 Planning and Task Decomposition in Agents Why an LLM that reasons well step by step still fails to produce a valid multi-step plan, and how decomposition, external planners, and replanning close the gap.
- 04 Tool Use and Function Calling How models invoke external tools to fetch data, run code, and take actions in the world.
- 05 Agent Evaluation Harnesses Single-output accuracy says nothing about an agent that takes thirty steps; evaluating agents means scoring trajectories, environment state, and reliability across runs.
- 06 Agent Memory Architectures An agent whose only memory is its context window is amnesiac between sessions; persistent memory is the architecture that decides what to keep, where, and how to retrieve it.
- 07 Agentic AI and ReAct From single tool calls to multi-step agents that plan, act, observe, and recover from errors.
- 08 Computer-Use Agents: Operating a GUI Through Pixels How agents that click and type on a real desktop differ from tool-calling agents, why GUI grounding is the bottleneck, and what OSWorld measured that API benchmarks cannot.
- 09 Long-Horizon Agent Reliability Why per-step accuracy compounds into task failure, how METR's time-horizon metric reframes agent capability, and which architectural moves actually raise the exponent.
- 10 Sandboxing and Least Privilege for Agents Why agent security has to be enforced outside the model, how capability scoping and human-in-the-loop gates work, and what the CaMeL design proves about the limits of prompting.
Claude Certified Architect Agentic loops, coordinator-subagent designs, tool interfaces and reliability patterns for Claude. 7 concepts · 34 cards
- 01 Agentic Loops and stop_reason Handling The agentic loop lifecycle - sending requests, inspecting stop_reason, executing tools, and appending results. The foundation of every autonomous Claude agent.
- 02 CLAUDE.md Configuration and Claude Code Workflows CLAUDE.md hierarchy, .claude/rules/ with glob patterns, custom commands and skills, plan mode vs direct execution, and CI/CD integration.
- 03 Programmatic Enforcement vs Prompt-Based Guidance When to use hooks and programmatic prerequisites for guaranteed compliance versus system prompt instructions for probabilistic guidance.
- 04 Prompt Engineering and Structured Output Patterns Explicit criteria, few-shot prompting, tool_use with JSON schemas, validation-retry loops, and the Message Batches API.
- 05 Tool Interface Design and MCP Integration Writing effective tool descriptions, structured error responses, MCP server scoping, and the distinction between MCP tools and resources.
- 06 Context Management and Reliability Patterns Context preservation across long interactions, escalation decision-making, error propagation in multi-agent systems, and information provenance.
- 07 Multi-Agent Coordinator-Subagent Architecture Hub-and-spoke multi-agent design with coordinator delegation, isolated subagent context, parallel execution, and iterative refinement loops.