Mistral Copilot-AI-driven text assistance

Example

Prompt engineering allows the copilot to analyze user-provided text, identify the best strategy, and generate desired outputs effectively.

Scenario

For instance, when prompted to classify customer inquiries, the copilot categorizes questions based on specified prompt instructions like 'country support' or 'technical support,' enabling quicker, more accurate response generation.

Example

The copilot can interact with external tools, databases, and APIs to access specific functions and data.

Scenario

A user working on payment data analysis can use function calling to retrieve the payment status and date given a transaction ID, thus accessing specific business data without direct access to internal systems.

Example

By generating embeddings (vector representations) for text data, the copilot can provide clustering, classification, and retrieval capabilities.

Scenario

When clustering or classifying text data for disease diagnosis or social media trends, the copilot generates accurate embeddings, leading to better classification outcomes.

Example

RAG integrates information retrieval with generation for more accurate and comprehensive answers.

Scenario

In software documentation or technical support, RAG helps retrieve the most relevant sections from a knowledge base, feeding this context into the prompt for clear, accurate responses.