AI Systems & LLM Application Engineering
Move beyond superficial chatbot wrappers. Learn to engineer robust Retrieval-Augmented Generation (RAG), autonomous multi-agent tool calling, and production latency optimization.
Learn how to force LLMs to output strict, schema-validated JSON with Pydantic & Zod, ensuring zero broken frontend UI layouts or invalid database inserts.
Master document chunking strategies, semantic embedding models, hybrid keyword-vector search with pgvector, and cross-encoder re-ranking.
LLM APIs, Structured Output & Function Calling
Goal: Interact with leading frontier models (Gemini 2.5, Claude 3.5, OpenAI) using native SDKs, streaming responses, and function tool execution.
Core Skills to Master
- Token Economics & Context Windows: Calculating input/output token pricing, context caching, and rate limit handling with exponential backoff.
- Structured Outputs: Enforcing strict Pydantic / Zod models on model completions.
- Function / Tool Calling: Teaching LLMs to trigger database lookups, weather APIs, and calculation scripts dynamically.
- Streaming SSE: Delivering real-time token streams to Next.js frontends via Server-Sent Events (SSE).
Vector Databases & Advanced RAG Architectures
Goal: Ground language models on proprietary company PDFs, databases, and codebases to eliminate hallucinations.
Core Skills to Master
- Chunking Strategies: Semantic chunking, recursive character splitting, and markdown header chunking.
- Embeddings: Generating vectors with OpenAI, Voyage, or Google text-embedding models.
- Vector Indexing with pgvector: Using PostgreSQL HNSW indexes for high-speed cosine and euclidean similarity lookups.
- Re-ranking: Using Cohere ReRank or cross-encoders to refine retrieved documents before prompt injection.
Multi-Agent Systems & Autonomous Workflows
Goal:Build autonomous multi-agent pipelines (Planner → Researcher → Coder → Reviewer) with memory and guardrails.
Core Skills to Master
- Agent Architectures: ReAct loops (Reasoning + Acting), LangGraph state graphs, and CrewAI delegation.
- Persistent Memory: Short-term session state vs. long-term memory stored in vector stores and relational DBs.
- Safety & Guardrails: Input sanitization, prompt injection defenses, and cost anomaly alerts.
The Capstone: Autonomous Knowledge Copilot & Research Agent
Architect an enterprise-ready AI copilot that ingests company documentation and performs multi-step research tasks:
System Specifications:
- Automatic ingestion pipeline converting PDF, DOCX, and web URLs into vector embeddings in pgvector.
- Hybrid search (PostgreSQL full-text tsvector + cosine distance embeddings) with re-ranking.
- Interactive agent capable of browsing documentation, summarizing findings, and generating cited output.
- Real-time token streaming and interactive UI artifacts rendered on Next.js 16.
- Observability dashboard tracking token expenditure, latency metrics, and user feedback ratings.
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