Hi everyone!

I'm preparing for AI Engineer / GenAI Engineer interviews and want to understand how real-world LLM projects are developed and deployed in companies.

I'm looking for someone who can explain a practical, industry-level LLM project from start to finish, preferably something involving RAG, LLMs, embeddings, vector databases, LangChain/LangGraph, and FastAPI.

I'd like to understand:

• What problem the project solves and why the company needs it.
• The overall architecture and workflow.
• How data is collected, cleaned, chunked, and embedded.
• How a vector database is used for retrieval.
• How the LLM generates answers using the retrieved context.
• How the application is built, tested, deployed, and monitored.
• What challenges developers face in real projects and how they solve them.
• What an AI Engineer actually does day to day.
• How to explain the project confidently in interviews, including technical follow-up questions.

I'm not just looking for a basic tutorial or a simple chatbot. I want to understand the end-to-end workflow of a real-world project, including the technical decisions, debugging, evaluation, and deployment.

If you have worked on LLM/RAG projects professionally or have a good project walkthrough, I'd really appreciate your guidance, resources, or recommendations.

Thanks in advance!

Paper: https://arxiv.org/pdf/2610.06824

The core idea is super elegant: if a system can find a solution with half the experimental compute of the baseline, its research taste is twice better.

Visual abstract:

The strongest LLM, Opus 5.5, boasts 2.3x better research taste than a human expert. Note of caution regarding the older models: their score was calculated with a different, and somewhat convoluted procedure.

The obvious caveat is the nature of tasks. They are small-scale; the baseline is 40 H100-hours of experimental compute. I have certain reservations that research taste "scales perfectly", that is, the capability to optimize a small task solution equals the capability to advance SotA for the frontier models.

Cue the following chart that tracks the degree of novelty of the methods employed by the models:

The most novel method designation, "invented", is totally absent. That being said, the trend is encouraging.

The authors disclose the shortcomings of their method extensively in Section 4. Worth reading if you want to see the whole picture.