LLM Agent for Natural Language Request Interpretation

implementationChallenge

Prompt Content

Develop a DSPy-powered agent using Llama 3.1 (via Replicate) that can accurately interpret diverse natural language requests from a simulated grid operator for ancillary services (e.g., frequency regulation, peak shaving, spinning reserve). The agent should extract key parameters such as service type, power magnitude, duration, start time, and priority. Provide examples of successful interpretations and your prompt engineering strategy.

Try this prompt

Open the workspace to execute this prompt with free credits, or use your own API keys for unlimited usage.

Usage Tips

Copy the prompt and paste it into your preferred AI tool (Claude, ChatGPT, Gemini)

Customize placeholder values with your specific requirements and context

For best results, provide clear examples and test different variations