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Prompt Engineering Training

Prompt Engineering Training: Getting the Most from LLMs

Hands-on training in systematic prompt design, few-shot, chain-of-thought, structured output and prompt evaluation techniques.

TL;DR

One-line answer: Prompt engineering training — teaches systematic prompt design, few-shot, chain-of-thought, structured output and prompt evaluation hands-on.

  • Systematic prompt design: role + context + instruction + examples + output format framework
  • Comparing few-shot, chain-of-thought, self-consistency and tree-of-thought techniques
  • Structured output (JSON, XML), schema validation, function calling for reliable responses
  • Prompt caching, evaluation harness, regression testing and cost optimization

Prompt Engineering Training — Programs

FAQ

Who should take prompt engineering training?
Three audiences: (1) Product teams and LLM app developers — for higher-quality responses and lower token cost. (2) Data teams and analysts — using LLMs for analytical queries. (3) AI educators and content writers — for large-scale content production. Beginner has no coding requirement, intermediate+ requires Python and API knowledge.
Should chain-of-thought (CoT) always be used?
No. CoT brings major quality gains for tasks requiring complex reasoning (math, logic, multi-step planning) — but for simple lookups or formatting it wastes tokens unnecessarily. The training teaches a decision matrix for 'when do I need CoT'; strategies differ for modern models (Claude 4.x, GPT-4o, o1).
What are the challenges of working with structured output (JSON)?
Three common issues: (1) Producing invalid JSON — function calling / structured output APIs solve this. (2) Skipping optional fields — schema defaults + 'always include' instructions solve this. (3) Truncation on long outputs — we teach max_tokens + chunked generation pattern.
What is prompt caching and how is it used?
Prompt caching stores repeated prompt prefixes (e.g. long system prompts, documents) server-side; subsequent calls see 50-90% cost reduction and up to 80% latency drop. The training covers Claude prompt caching (5min TTL) vs OpenAI prompt caching, cache hit ratio optimization and cost calculation.
How do you evaluate prompts?
Two layers: (1) Golden set evaluation — running a new prompt version against manually curated test pairs and diffing. (2) LLM-as-judge — another LLM scoring outputs against a rubric. The training shows the limits of each and how to use them in hybrid; includes setting up a regression test pipeline before production deployment.