LLMs work best when tasks can be converted into vectors — when meaning and relationships can be embedded in continuous space. But there are important areas where this approach falls short:
LLMs lack sensory experience and embodied reasoning. They struggle with:
Even if data is clean and neatly converted into tensors, models can still learn false patterns if the underlying data is flawed. Here's how to mitigate that risk:
This document provides a comprehensive guide for using the Utility Profit API to automate utility service registration and management.
Base URL: https://api-dev.utilityprofit.com/web
curl -X GET "https://api-dev.utilityprofit.com/web/activations/{activationItemId}"
This document provides detailed curl commands for interacting with the Form Fill Agent API, with real-world examples based on Colorado Springs Utilities and other providers.
Note: For general API documentation and endpoint details, see API-TESTING.md.
http://localhost:8000/api/v1
This document provides detailed information about the Form Fill Agent API endpoints, including example requests and responses.
Note: For more detailed curl commands with real-world examples and complete workflows, see CURL-CALLS.md.
http://localhost:8000
This document illustrates the flow of a Retrieval-Augmented Generation (RAG) system implementation using LangChain.
The RAG system combines document retrieval with language model generation to provide contextually relevant answers to user questions.