I've been working as a full-stack web developer using Node.js and Go. Recently, I've started wrapping the OpenAI API to create chatbots and various agents. Now, I'm looking to expand my knowledge into the AI field, specifically focusing on web-based applications and APIs. When I hear terms like LLM (Large Language Models) and embeddings, it seems like the primary work involves calling these APIs, storing embeddings in a database, and utilizing techniques like cosine similarity, which I understand falls under Retrieval-Augmented Generation (RAG). If I need more complex workflows, I can use tools like the Vercel AI SDK or OpenAI SDK to build agents, and perhaps automate some processes with n8n. However, I'm curious if there's more to consider or explore beyond this basic workflow.
1 Answer
You've got a solid grasp of the fundamentals! The real challenge lies in scaling those basic ideas into something reliable, such as ensuring prompt consistency, chunking for RAG setups, and handling potential failures. It might be worth exploring different LLM providers since some like Anannas allow you to swap models without rewriting everything, which can be useful when one model performs better on certain tasks than another.
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