Mastering LLM Application Development

The LLM Application Stack

Building with LLMs goes far beyond API calls. Production applications need retrieval, memory, guardrails, and evaluation.

Core Architecture Patterns

1. RAG (Retrieval-Augmented Generation)

The most common pattern. Index your documents in a vector database (Pinecone, Weaviate, Chroma), retrieve relevant chunks, and pass them as context to the LLM.

Key decisions:

• Chunking strategy (semantic vs fixed-size)
• Embedding model selection
• Hybrid search (vector + keyword)
• Re-ranking retrieved results

2. Agent Frameworks

Build autonomous agents that use tools, plan multi-step tasks, and maintain state. LangGraph, CrewAI, and AutoGen are leading frameworks.

Considerations:

• Tool design and error handling
• State management across turns
• Cost control and token budgeting
• Human-in-the-loop checkpoints

3. Fine-tuning vs Prompting

Use fine-tuning when you need consistent formatting, domain-specific knowledge, or cost reduction at scale. Use prompting for flexibility and rapid iteration.

Production Essentials

• Evaluation: Build automated eval suites with human-curated test cases
• Guardrails: Content filtering, output validation, and fallback responses
• Observability: Trace every LLM call with LangSmith, Phoenix, or custom logging
• Cost Management: Cache responses, use smaller models for simple tasks, batch requests

Project Ideas

1. Build a customer support chatbot with RAG over your docs
2. Create a code review agent that integrates with GitHub
3. Develop a research assistant that summarizes papers and extracts key findings

Career Outlook

LLM/AI engineers are among the highest-paid roles in tech. Median salary: $195K. Senior roles at AI companies: $300K+.