AI Automation & Prompt Engineering 10-Week Blueprint
Go beyond ChatGPT chats into building production AI applications. Architect Retrieval-Augmented Generation (RAG) vector pipelines, orchestrate multi-agent workflows with LangChain, and leverage Cursor AI for 5x engineering throughput.
Milestone Execution Timeline
Phase 1: LLM APIs, Structured Outputs & System Prompts
Master OpenAI and Claude API integrations, JSON mode, few-shot prompt crafting, and token budgeting.
- Chain-of-thought, few-shot prompting, and XML structural boundaries
- OpenAI & Claude API parameters (temperature, top_p, max_tokens)
- Enforcing strict JSON outputs with Pydantic schemas
- Token cost estimation and rate limit backoff algorithms
Phase 2: RAG Pipelines & Vector Database Retrieval
Build real-time knowledge retrieval engines connecting proprietary PDF documents, Notion pages, and SQL databases.
- Text embeddings (text-embedding-3-small, Cohere Embed)
- Chunking strategies (recursive character splitting, semantic chunking)
- Vector database indexing and hybrid search (Pinecone, ChromaDB, PGVector)
- Re-ranking search results with Cohere Re-rank for high contextual precision
Phase 3: Autonomous Multi-Agents & Production Deployment
Orchestrate agents capable of calling external APIs, browsing the web, and executing automated multi-step workflows.
- LangGraph / CrewAI multi-agent state machines and human-in-the-loop approvals
- Tool calling (function calling) with custom Python scripts and REST APIs
- Prompt injection defense (input validation, system prompt delimiters)
- Evaluating RAG quality using Ragas (Faithfulness, Answer Relevance)
Job-Readiness Graduation Checklist
Interactive Learning Tracker
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Common Career Transitions
Existing programming skills make API integration and vector database queries second nature.
Strong domain understanding of workflow bottlenecks enables designing high-ROI bots.
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