Prompt Engineering¶
This module is most useful when treated as systems design for model interaction, not as a bag of prompt hacks. The repo currently focuses on the practical subset you can actually apply in production workflows.
Actual Module Contents¶
What To Learn Here¶
How prompt quality changes task reliability
When few-shot examples are worth the extra tokens
How ReAct changes model behavior when tools are involved
Why structured outputs are better than post-hoc parsing
How to handle long contexts without blindly stuffing documents into prompts
How reasoning models change prompting strategy and output expectations
When prompt caching, context compression, and schema-first design matter more than clever wording
Recent 2026 Prompting Topics¶
Reasoning-model prompting for deliberate models that think longer before answering
Context engineering: prompt layering, retrieval packing, and instruction hierarchy
Prompt caching and prefix reuse to reduce cost and latency
Structured generation with JSON schema constraints and tool-first interfaces
Eval-driven prompt iteration instead of subjective prompt tweaking
Recommended Study Order¶
Start with 01_basic_prompting.ipynb
Move to 02_chain_of_thought.ipynb for reasoning patterns
Study 03_react_prompting.ipynb before Phase 15 agents
Use 05_structured_outputs_dspy.ipynb to connect prompting with reliability
Finish with 06_long_context_strategies.ipynb before deeper RAG and agent work
Learning Advice¶
Prompting is not a substitute for retrieval, fine-tuning, or evaluation.
If a task fails, ask whether the real issue is missing context, poor tools, or missing schema constraints.
Keep examples and output formats explicit; ambiguity is expensive.
Good Follow-On Projects¶
A structured extraction pipeline with validation
A multi-step research assistant using ReAct
A long-context summarizer that compares chunk-then-summarize vs direct prompting