21 docs tagged with "prompt-engineering"

Master self-refinement, Tree of Thought, ReAct, meta-prompting, and other advanced techniques for reliable, sophisticated LLM behavior in production.

Learn how to unlock multi-step reasoning in LLMs by making them think out loud - and why this simple technique dramatically improves accuracy on complex tasks.

Master few-shot example selection, chain-of-thought reasoning, self-consistency decoding, and when to use each technique for reliable LLM outputs.

Master in-context learning by providing carefully selected examples that demonstrate the exact behavior you want - without any model fine-tuning.

Python patterns for building production LLM applications - API integration, streaming, prompt engineering, token management, tool use, and vector search.

Master the art and science of communicating with large language models - from basic zero-shot instructions to automated prompt optimization with DSPy.

Systematic methodology for diagnosing and fixing prompt failures - isolation, ablation, root cause analysis, and building a regression test suite.

Master the first principles of prompt engineering - clarity, specificity, task framing, structural markers, and the systematic principles behind effective LLM instructions.

Engineering system prompts, few-shot examples, and robust prompt pipelines for production LLMs.

Understand how prompt injection attacks work, why they're hard to defend against, and how to build LLM systems that are resistant to manipulation.

Understand prompt injection attack taxonomy, detection strategies, defense layers, and sanitization techniques for production LLM systems.

Move beyond manual prompt engineering to automated, evaluation-driven optimization - using APE, OPRO, and DSPy to build LLM pipelines that improve themselves.

Build maintainable, production-grade prompt systems with Jinja2 templates, variable injection, modular composition, and reusable prompt libraries.

Building maintainable prompt systems in Python - template engines, versioning, testing prompts, few-shot construction, and prompt injection defense.

Treat prompts as code - semantic versioning, A/B testing, rollback strategies, and prompt registries for production LLM systems.

Learn how to build LLM agents that reason and act by interleaving thought and tool calls - the architectural pattern behind every modern AI assistant.

Reliably extract structured data from LLMs using JSON mode, function calling, Pydantic validation, and constrained decoding - the backbone of production LLM pipelines.

Master the architecture of LLM conversations - how to design system prompts, manage context windows, and build production-grade context management systems.

Design production-grade system prompts and AI personas - the 6-component anatomy, dynamic context injection, behavioral constraints, tone configuration, and persona stability testing.

Explore multiple reasoning paths simultaneously using Tree-of-Thought - the technique that enables LLMs to backtrack, evaluate alternatives, and solve problems that defeat linear chain-of-thought.

Learn how to elicit reliable behavior from LLMs using only instructions - no examples required - by mastering prompt anatomy, role personas, and format control.