Review DSPy code for correctness and best practices. Use when you want a code review of your DSPy program, need to check if your AI code follows best practices, want to find anti-patterns in your DSPy usage, or need a quality audit of your AI implementation. Also use for DSPy code review, is my DSPy code correct, review my AI code, best practices check, DSPy anti-patterns, code quality audit, am I using DSPy right, sanity check my AI code, peer review my DSPy program, does this follow DSPy conventions.
Verify and validate AI output before it reaches users. Use when you need guardrails, output validation, safety checks, content filtering, fact-checking AI responses, catching hallucinations, preventing bad outputs, or quality gates. Also used for - AI output looks right but is wrong, how to validate JSON from LLM, LLM returns invalid data, catch bad AI outputs before users see them, output quality gate, AI guardrails for production, verify LLM did not hallucinate fields, post-processing LLM responses. Uses dspy.Refine (iterative with feedback) and dspy.BestOfN (sampling, pick best).
Pick the right DSPy module and architecture for your AI feature. Use when you are not sure whether to use Predict, ChainOfThought, ReAct, or a pipeline, need to choose between DSPy patterns, want architecture advice for your AI feature, or are deciding between a single module and a multi-step pipeline. Also use for which DSPy module should I use, Predict vs ChainOfThought, when to use ReAct, single module vs pipeline, DSPy architecture decision, CoT vs PoT vs ReAct, do I need a pipeline, module selection guide, DSPy pattern selection, how to structure my DSPy program.
Normalize and fix messy data fields using AI. Use when normalizing addresses, standardizing company names, fixing inconsistent date formats, cleaning CSV data before import, correcting typos in bulk data, normalizing phone number formats, standardizing job titles, cleaning up free-text fields, data quality improvement with AI, fixing formatting inconsistencies, bulk data normalization, preparing messy data for analysis, AI-powered data wrangling.
Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Also used for GPT-4 costs too much for production, AI bill keeps growing, how to reduce OpenAI costs, optimize LLM token usage, smart model routing saves money, prompt is too long and expensive, cheaper than GPT-4 with same quality.
Describe your AI problem and get routed to the right skill with a ready-to-use prompt. Use when you are not sure which ai- skill to use, want help picking the right approach, or just want to describe what you need in plain language. Also use this when someone says I want to build an AI that..., how do I make my AI..., or describes any AI/LLM task without naming a specific skill, I need AI but do not know where to start, which AI pattern should I use, what is the best way to add AI to my app, recommend an AI approach, AI feature discovery, too many AI options, overwhelmed by AI frameworks, just tell me what to build, new to DSPy, beginner AI project help, which LLM pattern fits my use case, confused about AI architecture, help me figure out my AI approach.
Fine-tune models on your data to maximize quality and cut costs. Use when prompt optimization hit a ceiling, you need domain specialization, you want cheaper models to match expensive ones, you heard fine-tuning will make us AI-native, you have 500+ training examples, or you need to train on proprietary data. Also use when you have spent weeks of manual iteration with no systematic improvement path, or manual prompt tuning got you to a working system but quality plateaued. Covers DSPy BootstrapFinetune, BetterTogether, model distillation, and when to fine-tune vs optimize prompts, LoRA vs full fine-tune, when to fine-tune vs few-shot, distill GPT-4 into a smaller model, teacher-student model training, custom model training with DSPy, model distillation, make a cheap model as good as GPT-4.
Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Also used for spent hours tweaking prompts, trial and error prompt engineering is not working, quality plateaued early, stale prompts everywhere in your codebase, my AI is only 60% accurate, how to measure AI quality, AI evaluation framework, benchmark my LLM, prompt optimization not working, systematic way to improve AI, AI accuracy plateaued, DSPy optimizer tutorial, MIPROv2 optimization, how to go from 70% to 90% accuracy.