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AI Engineer Expert

Turns ML models into production features that actually scale — RAG, agents, eval, infra.

Starter prompts

4 ways to start with AIEng.

Click any → opens in app with prompt pre-loaded
RAG
→
Production-ready
▸ Preview prompt
Design a production RAG over our 50k-doc knowledge base — chunking, embeddings, retrieval evals, citations, fallback when retrieval is empty.
Agent
→
Tool use
▸ Preview prompt
Build a tool-using agent that triages support tickets — what tools, what guardrails, how I measure quality before shipping.
Cost
→
Half the spend
▸ Preview prompt
Our OpenAI bill is $40k/month. Lay out a plan to cut 50% with no quality drop — caching, routing, smaller models.
Eval
→
Catch regressions
▸ Preview prompt
Set up an eval pipeline that runs on every prompt or model change and blocks deploys on a drop.
What it does

Tasks AIEng ships every week.

AI features
  • RAG with retrieval evals
  • Tool-using agents
  • Streaming + token caching
  • Guardrails + safety
Operations
  • Inference cost control
  • Latency budgeting
  • Eval + regression
  • Model swap + A/B
Worked sample

A real AIEng chat.

No edits · no cherry-picks
You
RAG is hallucinating. What do I check first?
AI

Retrieval, not generation. Open the top-K chunks for a failing query. If the answer isn't in there, fix chunking, embeddings, or query rewriting. If it is and the model still hallucinates, tighten the prompt to refuse outside context and add a citation check. 80% of RAG failures are retrieval.

✦ grounded in your files + site · memory on · 200M proxies + 4,000 tools
Pairs well with

AIEng works best alongside these 3.

Prompt Engineer Expert Writes contracts between humans and models — measurable, testable, production-grade. Data Engineer Expert Builds pipelines that turn raw data into trusted, analytics-ready assets — ETL, Spark, dbt, streaming. Multi-Agent Systems Architect Treats a team of agents like a distributed system — topology, trust, observability, failure recovery.
Put AIEng on your team.
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