ai-native-development

RAG (retrieval-augmented generation) integrates LLMs with vector databases to retrieve relevant documents before answering questions. Store embeddings in Pinecone, Weaviate, or Chroma, then query them to augment prompts sent to OpenAI or Anthropic models, enabling accurate responses grounded in your knowledge base.

LangChain abstracts LLM providers, letting you switch between OpenAI, Anthropic, and open-source models without rewriting core logic. Build once, swap providers by changing configuration to compare costs, latency, and model capabilities across your AI pipeline.

Generate embeddings from your text using LLM APIs, then ingest them into a vector database like Pinecone, Weaviate, or Chroma for semantic search and retrieval. This enables similarity-based queries to find relevant documents for RAG pipelines and recommendation systems.

Agentic workflows use LLMs as decision-makers that select and orchestrate tools through function calling. Define available functions, let the model reason about which to call, execute them, and feed results back—enabling autonomous agents for complex tasks across multiple systems.

Instrument LLM calls to log token usage, latency, and model selection across your pipeline. Monitor embeddings, vector searches, and agent decisions to identify bottlenecks and cost drivers, then optimize prompt design, batch operations, or model selection to reduce expenses.

Combine RAG with citation tracking by storing document metadata alongside embeddings. When retrieving documents for context, attach source references to generation prompts so the assistant includes citations in responses, providing transparency and verification for users.