AI-Engineering Playbooks

Key Tools

Implementation Steps

1. 1Start with v0 to generate UI components from natural language descriptions
2. 2Use Cursor with Claude for real-time AI pair programming and code completion
3. 3Leverage Claude Code for complex refactoring and codebase-wide changes

Pro Tips

• 💡Be specific in your prompts - describe exactly what you want
• 💡Review AI-generated code before committing

Key Tools

Implementation Steps

1. 1Standardize on AI IDE (Cursor or Windsurf) across the team
2. 2Enable GitHub Copilot for baseline code completion
3. 3Use Cline for autonomous multi-file refactoring tasks

Pro Tips

• 💡Establish team guidelines for AI usage
• 💡Review AI PRs with extra scrutiny initially

Key Tools

Implementation Steps

1. 1Use Bolt.new or Replit Agent to scaffold complete applications from descriptions
2. 2Refine frontend with v0 for polished UI components
3. 3Use Claude Code for complex backend logic and API design

Pro Tips

• 💡Start with a clear product vision before prompting
• 💡Test generated APIs thoroughly

Key Tools

Implementation Steps

1. 1Design agent workflow and decision points with LangGraph
2. 2Implement structured outputs with Instructor for reliable parsing
3. 3Add long-term memory using Pinecone vector storage

Pro Tips

• 💡Start simple - single agent before multi-agent
• 💡Log everything - agent debugging is hard

Key Tools

Implementation Steps

1. 1Define agent roles and responsibilities clearly
2. 2Choose orchestration pattern: hierarchical, peer-to-peer, or hybrid
3. 3Implement shared memory and context with Weaviate

Pro Tips

• 💡Define clear handoff protocols between agents
• 💡Limit agent autonomy based on task risk

Key Tools

Implementation Steps

1. 1Design document ingestion pipeline with proper chunking
2. 2Set up vector database (Pinecone for managed, Qdrant for self-hosted)
3. 3Implement hybrid search combining semantic and keyword retrieval

Pro Tips

• 💡Chunk size matters - experiment with different sizes
• 💡Hybrid search often outperforms pure semantic search

Key Tools

Implementation Steps

1. 1Set up streaming chat UI with Vercel AI SDK and shadcn/ui
2. 2Implement conversation memory with Upstash Redis
3. 3Add system prompts for personality and behavior

Pro Tips

• 💡Stream responses for better UX
• 💡Truncate old messages to stay within context limits

Key Tools

Implementation Steps

1. 1Instrument LLM calls with Langfuse for tracing
2. 2Add Helicone proxy for cost tracking and caching
3. 3Set up Promptfoo for prompt regression testing

Pro Tips

• 💡Start with basic tracing, add evaluation later
• 💡Track costs from day one

Key Tools

Implementation Steps

1. 1Define output schemas with Zod/Pydantic
2. 2Use Instructor for automatic schema enforcement
3. 3Add Guardrails AI for content validation

Pro Tips

• 💡Start with simple schemas, add complexity gradually
• 💡Always validate before using LLM output

Key Tools

Implementation Steps

1. 1Set up development environment with Cursor for AI-assisted coding
2. 2Use v0 to rapidly prototype UI components and layouts
3. 3Configure Supabase for database and Clerk for authentication

Pro Tips

• 💡Focus on core value proposition first
• 💡Use AI to accelerate not replace thinking

Key Tools

Implementation Steps

1. 1Implement LangChain/LangGraph for complex AI workflows
2. 2Set up Langfuse for LLM tracing and debugging
3. 3Add Helicone for cost tracking and caching

Pro Tips

• 💡Observability is not optional in production
• 💡Set cost budgets and alerts early

Key Tools

Implementation Steps

1. 1Set up GitHub Actions for automated testing and deployment
2. 2Implement infrastructure as code with Terraform or Pulumi
3. 3Containerize applications with Docker

Pro Tips

• 💡Start simple with Vercel/Railway before Kubernetes
• 💡Infrastructure as code from the start

Key Tools

Implementation Steps

1. 1Set up data ingestion with Fivetran connectors
2. 2Transform and model data with dbt
3. 3Process large datasets with Databricks

Pro Tips

• 💡Start with simple batch, add streaming later
• 💡Version your embeddings