LLM App Patterns

What To Know

The listings emphasize practical LLM applications: workflow automation, RAG, agents, tool calling, evaluation, and production reliability.

The useful posture is sober: use LLMs where they help with language, ambiguity, summarization, classification, drafting, and search. Keep humans in control where the outcome matters.

Core Patterns

• Summarization of tickets, docs, transcripts, and customer records.
• Classification and routing.
• Structured extraction into JSON.
• RAG with citations.
• Draft recommendations for human approval.
• Tool calling for bounded actions.
• Evaluation sets for repeated tests.
• Logging prompts, model outputs, and user decisions.

Questions To Ask Before Adding AI

• What decision or task is being improved?
• What source material should ground the answer?
• What is the cost of being wrong?
• Who approves the output?
• How will quality be evaluated?
• What should happen when confidence is low?

Interview Angle

Strong phrasing:

I try to place LLMs inside a workflow with source grounding, human review, and feedback loops, rather than treating the model response as the product.