Advanced Prompt Engineering
Learn advanced prompt engineering through RunLocalAI's practical lens: prompts, chain of thought, dspy and optimization, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
- B005
Course I012: Advanced Prompt Engineering
Why this course exists
Prompting works—at first. Simple instructions produce reasonable outputs for simple tasks. But production systems demand reliability. A single prompt that works perfectly on one model version may degrade silently when the underlying model updates. Basic prompting gives no mechanism to measure, version, or systematically improve quality.
This course addresses the gap between "the model does what I ask" and "the system produces consistent, high-quality outputs that can be validated and improved over time." The techniques covered here—chain-of-thought variants, tree-of-thought reasoning, self-consistency sampling, and the DSPy framework—represent accumulated knowledge from operators who have deployed LLM-based systems at scale.
These methods are not theoretical. Every technique has specific failure modes, computational costs, and applicability constraints. The goal is to build working intuition about when each approach helps and when it adds complexity without benefit.
What you will know after
- How to decompose complex tasks into reasoning chains that models can follow reliably
- When chain-of-thought improves output quality and when it introduces unnecessary verbosity
- How tree-of-thought exploration outperforms greedy decoding for certain problem types
- Why self-consistency doesn't simply "vote" but rather measures output distribution stability
- Patterns for sequencing prompts where each step depends on previous outputs
- How to construct prompts dynamically from modular components rather than static strings
- The DSPy philosophy: treating prompts as compiled artifacts rather than hand-written text
- How DSPy Signatures abstract input/output specifications from implementation details
- How DSPy optimizers tune prompts and LM configurations automatically against labeled data
- 01Beyond Basic PromptingAdvanced prompting shifts from implicit assumptions about model behavior to explicit specification and measurement of outputs.15 min
- 02Advanced Chain-of-ThoughtBasic CoT asks for reasoning; advanced CoT asks for verifiable reasoning with explicit stages and anchoring to source facts.15 min
- 03Tree-of-ThoughtTree-of-thought converts the reasoning problem from a single-path generation to a best-of-n exploration, with the tradeoff of multiplied compute and evaluation bias.20 min
- 04Self-ConsistencySelf-consistency leverages the assumption that correct reasoning converges through different paths, treating answer distribution as a proxy for answer confidence.20 min
- 05Prompt Chaining PatternsPrompt chaining decomposes complex workflows into specialized steps with explicit inputs and outputs, trading simplicity for control and debuggability.20 min
- 06Dynamic Prompt ConstructionDynamic prompt construction separates prompt architecture from prompt content, enabling component reuse and adaptive behavior without code changes.20 min
- 07DSPy IntroductionDSPy abstracts prompt behavior into declared signatures and composed modules, compiling prompts from data rather than hand-coding them.20 min
- 08DSPy SignaturesDSPy signatures declare contracts between inputs and outputs through typed field definitions, separating behavioral specification from implementation details.20 min
- 09DSPy OptimizersDSPy optimizers search for prompt configurations that maximize evaluation metrics on training data, with the critical caveat that optimized prompts may overfit to training distribution.20 min
- 10Automated Prompt TuningAutomated tuning amplifies whatever bias exists in the evaluation criteria—a prompt that scores 95% on a flawed rubric will fail in production.15 min
- 11Prompt Version ControlPrompt changes are invisible unless explicitly logged—every version should include a diff explaining what changed and why, not just the new content.20 min
- 12Prompt Testing FrameworkTests written from production failures are 10x more valuable than synthetic test cases—real user queries expose edge cases that imagination doesn't.20 min
- 13Regression TestingRegression detection requires storing historical metrics—without baseline measurements, degradation is invisible until users complain.20 min
- 14Cross-Model PortabilityPortability testing catches 60% of cross-model failures, but the remaining 40% are subtle behavioral differences that only appear in production-like evaluation.20 min
- 15Prompt SecurityInput sanitization is necessary but not sufficient—injection attacks increasingly use context-aware techniques that bypass pattern matching.20 min
- 16Cost-Per-Token OptimizationToken optimization is bounded by minimum information content—removing tokens beyond that threshold degrades output quality faster than it reduces cost.20 min
- 17Prompt CompressionCompression that doesn't degrade task performance is essentially free improvement—same output, lower cost and latency.25 min
- 18Prompt Framework ProjectA framework without tests is just scaffolding—a production prompt system requires the same rigor as software development: version control, testing, CI/CD, and monitoring.25 min