Building LLM Applications: From RAG to Autonomous Agents
Large Language Models have evolved from chat interfaces to powerful platforms for building real applications. This guide walks through the key architectural patterns you need to know.
Retrieval-Augmented Generation (RAG)
RAG combines a language model with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems can search documents, databases, or the web for relevant information before generating a response.
Why RAG Matters
- Eliminates hallucination by grounding responses in real data
- Enables question-answering over your private documents
- Keeps responses current without retraining
Vector Databases
RAG relies on vector databases like Pinecone, Weaviate, or pgvector to store and search embeddings — numerical representations of text meaning.
LLM Agents
Agents extend LLMs with tools and autonomy. An agent can:
- Search the web for current information
- Write and execute code
- Call APIs
- Make decisions about what to do next
Building for Production
Monitoring: Track latency, token usage, and response quality.
Caching: Cache common queries to reduce costs.
Guardrails: Add content filters and validation layers.
The 212AY Approach
Our "Build with LLMs" programme teaches students to ship real API endpoints, RAG systems, and agent architectures. By the end, students have a portfolio of production-ready applications.