Large Language Models for Developers

For software engineers, interacting with Large Language Models (LLMs) is moving beyond simple prompt construction. To build reliable apps, we need to design structured LLM architectures.

Retrieval-Augmented Generation (RAG)

RAG is the standard approach for injecting private enterprise data into a general LLM without undergoing expensive fine-tuning.

[User Query] -> [Vector Search in DB] -> [Retrieve Relevant Context] -> [Prompt Construction] -> [LLM Response]

Designing Vector Pipelines

1. **Document Chunking**: Break large text documents into overlapping segments (e.g. 500 characters with 50-character overlap).

1. **Embedding Generation**: Convert text chunks into high-dimensional vector embeddings using models like OpenAI text-embedding-3-small.

1. **Storage**: Index vectors in specialized databases (Pinecone, pgvector) for rapid cosine similarity searches.

1. **Context Injection**: Feed search results directly into the system prompt to guide LLM inference.