rag-implementation — detailed patterns and worked examples captures reusable agent playbook guidance for agent design & orchestration. Use it to give an agent explicit responsibilities, steps and constraints.

1. Keep It DRY : Use templates to avoid repetition 2. Validate Early : Check variables before rendering 3. Version Templates : Track changes like code 4. Test Variations : Ensure templates work with diverse inputs 5. Document Variables : Clearly specify required/op. Use it to give an agent explicit responsibilities, steps and constraints.

1. Establish Baseline : Always measure initial performance 2. Change One Thing : Isolate variables for clear attribution 3. Test Thoroughly : Use diverse, representative test cases 4. Track Metrics : Log all experiments and results 5. Validate Significance : Use st. Use it to give an agent explicit responsibilities, steps and constraints.

Few-shot learning enables LLMs to perform tasks by providing a small number of examples (typically 1-10) within the prompt. This technique is highly effective for tasks requiring specific formats, styles, or domain knowledge. Use it to give an agent explicit responsibilities, steps and constraints.

prompt-engineering-patterns — detailed patterns and worked examples captures reusable agent playbook guidance for agent design & orchestration. Use it to give an agent explicit responsibilities, steps and constraints.

Chain-of-Thought (CoT) prompting elicits step-by-step reasoning from LLMs, dramatically improving performance on complex reasoning, math, and logic tasks. Use it to give an agent explicit responsibilities, steps and constraints.

llm-evaluation — detailed patterns and worked examples captures reusable agent playbook guidance for agent design & orchestration. Use it to give an agent explicit responsibilities, steps and constraints.

langchain-architecture — detailed patterns and worked examples captures reusable agent playbook guidance for agent design & orchestration. Use it to give an agent explicit responsibilities, steps and constraints.

Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration. Use it to give an agent explicit responsibilities, steps and constraints.

hybrid-search-implementation — templates and worked examples captures reusable agent playbook guidance for agent design & orchestration. Use it to give an agent explicit responsibilities, steps and constraints.