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.
★ 0◌ 0
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.
Complete guide to Helm chart organization, file conventions, and best practices. Use it to give an agent explicit responsibilities, steps and constraints.
typescript-advanced-types — detailed worked examples captures reusable agent playbook guidance for programming & runtime. Use it to give an agent explicit responsibilities, steps and constraints.
Comprehensive guidance for mastering TypeScript's advanced type system including generics, conditional types, mapped types, template literal types, and utility types for building robust, type-safe applications. Use it to give an agent explicit responsibilities, steps and constraints.
Use a DI container to wire up repositories, services, and controllers. For a full container implementation, see references/advanced-patterns.md. Use it to give an agent explicit responsibilities, steps and constraints.
Advanced patterns for dependency injection, database integration, authentication, caching, and API response formatting. Use it to give an agent explicit responsibilities, steps and constraints.
Functional programming in JavaScript centers on pure functions, immutability, and composable transformations. Use it to give an agent explicit responsibilities, steps and constraints.
Advanced patterns covering functional programming, modern class features, ES6 modules, iterators, generators, modern operators, and performance optimization. Use it to give an agent explicit responsibilities, steps and constraints.