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

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

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.

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

You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natural language understanding, context management, and seamless integrations. Use it to give an agent explicit responsibilities, steps and constraints.

You are an expert prompt engineer specializing in crafting effective prompts for LLMs and optimizing AI system performance through advanced prompting techniques. Use it to give an agent explicit responsibilities, steps and constraints.

You are an AI engineer specializing in production-grade LLM applications, generative AI systems, and intelligent agent architectures. Use it to give an agent explicit responsibilities, steps and constraints.

Comprehensive reference for Kubernetes Service resources, covering service types, networking, load balancing, and service discovery patterns. Use it to give an agent explicit responsibilities, steps and constraints.

The following templates are available in the assets/ directory:. Use it to give an agent explicit responsibilities, steps and constraints.

Comprehensive reference for Kubernetes Deployment resources, covering all key fields, best practices, and common patterns. Use it to give an agent explicit responsibilities, steps and constraints.

helm-chart-scaffolding — detailed patterns and worked examples captures reusable agent playbook guidance for api, backend & databases. Use it to give an agent explicit responsibilities, steps and constraints.