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Use when the task requires precise computation, math, or data manipulation — the LM writes Python code that executes in a sandbox instead of reasoning in natural language. Common scenarios - math word problems, data manipulation tasks, precise calculations the LLM gets wrong in natural language, statistical analysis, or any task where writing and executing code gives better results than reasoning

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

Make AI solve hard problems that need planning and multi-step thinking. Use when your AI fails on complex questions, needs to break down problems, requires multi-step logic, needs to plan before acting, gives wrong answers on math or analysis tasks, or when a simple prompt is not enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery re

Use when you need to customize how DSPy formats prompts for a specific provider — switching from chat to completion format, forcing JSON output, or debugging prompt rendering issues. Common scenarios - debugging why your prompt looks wrong when sent to the model, switching from OpenAI to Anthropic and the formatting breaks, forcing the model to return valid JSON instead of markdown, working with c

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 mo

Use when basic BootstrapFewShot is not enough and you want to search over multiple candidate demo sets — better results at the cost of more LM calls. Common scenarios - BootstrapFewShot alone is not reaching target accuracy, you want to search over multiple candidate demo sets and pick the best, optimizing for tasks where example selection matters a lot, or when you have compute budget for a more

Build multiple AI agents that work together. Use when you need a supervisor agent that delegates to specialists, agent handoff, parallel research agents, support escalation (L1 to L2), content pipeline (writer + editor + fact-checker), or any multi-agent system. Also used for CrewAI alternative, AutoGen alternative, LangGraph multi-agent, agents that talk to each other, specialist agents with a su

Scaffold a new AI feature powered by DSPy. Use when adding AI to your app, starting a new AI project, building an AI-powered feature, setting up a DSPy program from scratch, or bootstrapping an LLM-powered backend. Also used for DSPy quickstart, DSPy hello world, first DSPy program, getting started with DSPy, new to AI development, add AI to existing Python app, AI feature from zero to working, sc

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, s

Use when you have already tried prompt-only optimization and want the next level — jointly tuning prompts and model weights for maximum quality. Common scenarios - you have maxed out prompt optimization and need the next level, combining instruction tuning with weight tuning for maximum quality, making a small model match a large model through joint optimization, or squeezing the last few percent

Use Langtrace for DSPy observability and tracing. Use when you want to set up Langtrace, langtrace-python-sdk, auto-instrument DSPy, trace DSPy calls, LLM observability, app.langtrace.ai, or self-hosted tracing. Also used for langtrace setup, langtrace API key, pip install langtrace-python-sdk, DSPy tracing, auto-instrument DSPy, langtrace self-hosted, langtrace docker, trace LM calls, langtrace v

Run DSPy with local models using Ollama — no API key needed. Use when you want to run DSPy locally, use Ollama, set up a local LLM, run offline, or configure local model parameters. Also used for ollama, local model, run LLM locally, llama local, self-hosted LLM, ollama serve, ollama_chat, local inference, run DSPy offline, no API key needed, ollama pull, ollama list, ollama rm, num_ctx, ollama co