Generative AI Patterns

Topic

Name: Generative AI patterns

Why it matters for FDE roles: FDE listings often ask for practical AI delivery, not abstract model knowledge. Patterns help translate a messy customer problem into a small workflow that can be built, evaluated, and deployed.

Plain-English Definition

Generative AI patterns are repeatable ways to use an AI model inside software. The model might summarize, classify, extract structured fields, draft text, answer from retrieved sources, call tools, or recommend next actions.

Where It Shows Up

• Job listing signal: LLM applications, AI workflows, copilots, agents, automation, RAG, evaluation.
• Real customer scenario: A support team wants ticket summaries, escalation reasons, and suggested next steps across scattered internal notes.

Core Concepts

• Summarization for long records, tickets, calls, and documents.
• Classification and routing for status, urgency, topic, owner, or risk.
• Structured extraction into predictable fields.
• RAG for grounded answers with citations.
• Tool calling for bounded actions against APIs and internal systems.
• Human review for suggestions that affect customers, money, compliance, or production data.

Failure Modes

• Treating a model response as truth without source grounding.
• Over-automating a workflow before the business rules are clear.
• No evaluation set, so quality is judged by vibes.
• Missing audit trails for prompts, sources, outputs, and human decisions.
• Building a demo that works on happy-path examples but fails on ambiguous records.

Interview Language

One sentence I could say in an interview:

I think in LLM patterns first: what should be summarized, extracted, retrieved, drafted, or acted on, and where does the workflow need grounding, evaluation, and human review?

Links / References

• LLM App Patterns
• RAG And Knowledge Systems