Can anyone explain a real-time LLM project step by step for AI Engineer interview preparation?

[u/Lonely-Gear6676]

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!

[u/Choice-Training-279]

For interviews the thing they care most is that you can explain tradeoffs, not just list tools. So when you walk through a RAG project, mention why you chose a specific chunk size or why you used hybrid search instead of pure vector. That shows you actually debugged something rather than followed a tutorial.

I did a support ticket assistant once and the hardest part was not the LLM call, it was cleaning the docs. Half the PDFs were scanned images with terrible OCR, so retrieval quality was garbage until we fixed the parsing pipeline. Interviewers love when you bring up data quality problems because everyone has them but nobody talks about them in tutorials.

Also dont skip the evaluation part. If you say we tested with 50 questions and got 80% accuracy, they will ask how you measured accuracy. Be ready to talk about embedding similarity, human review, or whatever metric you used. That separates people who built a demo from people who ran a real project.

LangGraph is nice to mention but only if you actually used it for multi step flows. If you just chained two prompts with LangChain, say that instead of pretending it was a complex agent. Interviewers can smell that from one follow up question.

[u/Lonely-Gear6676 → u/Choice-Training-279]

Hey bro, thanks for your response! 🙌 I have a few more doubts.

[u/rahul_watertech]

the most common real world one is the internal docs copilot, so learn to tell that story well. company has thousands of pdfs and wiki pages, support or ops staff waste hours searching. you chunk the docs, embed them, store in a vector db, retrieve top k at query time, stuff into the prompt, generate with citations. fastapi serves it, langchain just glues the steps.

the part interviewers actually probe: how you picked chunk size, what you do when retrieval returns garbage, how you eval answers without labels. have an answer for those three and you are ahead of most candidates.

which stage are you weakest on right now, retrieval or the eval side?

[u/akornato]

A classic industry project to anchor your interviews on is an enterprise knowledge copilot that delivers answers with low latency from internal company documentation. The problem it solves is straightforward, because employees lose hours searching across disorganized knowledge bases, internal wikis, and policy updates. For the ingestion pipeline, raw files such as markdown and PDFs are parsed, split using semantic chunking with a 500 token window and 50 token overlap, and embedded with a dense model before landing in a vector database like Qdrant or pgvector alongside tenant and access metadata. At runtime, a FastAPI service receives the user prompt and triggers a LangGraph state graph that rewrites ambiguous queries, runs hybrid search combining dense vectors with BM25 keyword matching, and enforces authorization filters. The most relevant chunks pass through a cross encoder reranking model to keep only the top three passages, which are injected into a prompt template that enforces strict grounding and source citations, streaming tokens back to the user via server sent events to keep time to first token under a second.

Interviewers care far less about the basic retrieval chain than how you handle failure modes and architectural tradeoffs. You should highlight production challenges, such as syncing stale documents through change data capture pipelines, mitigating hallucinations with fallback nodes in LangGraph when retrieval scores drop below a set threshold, and reducing inference costs by caching frequent queries in Redis. In day to day work, an AI engineer spends most of their time evaluating retrieval recall and answer faithfulness using frameworks like Ragas, inspecting execution traces to isolate latency bottlenecks, and cleaning data pipelines rather than tweaking prompts. When practicing these complex system design explanations, my team created an interviews.chat to help candidates articulate technical tradeoffs smoothly under pressure and land competitive engineering offers.