Mistral AI Masterclass: Enterprise LLMs & RAG Systems

Explore Mistral small, medium, and large models, their capabilities and trade-offs in production contexts. Use cascade routing; start with small, escalate to medium, then large to balance accuracy and cost.

Build production-grade AI systems by integrating Mistral with a robust API client, Redis caching, rate limiting, Celery queues, health checks, and Postgres-backed conversation history to manage context and costs.

Define the business problem and success criteria for a company copilot, then set up a VS Code project, Python virtual environment, and dependencies to prepare for Mistral API connection.

Ingest real company documents (PDFs, docs, slides) using Mistral OCRAPI, extract text, chunk 150 to 200 tokens with overlap, generate embeddings, and index in Faze for fast retrieval.

Learn to prevent hallucinations by grounding answers in supplied documents with explicit citations, using a LangChain-based rag pipeline and Mistral's embed model.

Prepare help center content with Flowwise by organizing relevant PDFs, ensuring searchable text with OCR, adding metadata, and using chunking for accurate retrieval in llms and rag.

Implement retrieval augmented generation with Mistral embeddings and a Pinecone vector store, grounding AI responses in your documents, using upserted chunks and Neon Postgres for record management.

Review results and improve accuracy of automated research by validating sources, refining the workflow, and configuring the Mistral AI model, SERPAPI, and Google Sheets with a weekly schedule.

Collect real meeting audio and notes with transcription and action items via a Google Drive triggered workflow that downloads audio to Assembly AI for transcription.

Define content goals and brand constraints to shape enterprise LLMs content generation; set brand voice, audience, and goals in a scalable n8n-powered social media automation workflow.

Automate content generation while enforcing human oversight through a two-branch workflow using Google Sheets for draft tracking and approvals, leading to scheduled LinkedIn posting after approval.

Learn to implement prompt caching and response reuse by replacing the simple memory with Redis in n8n, storing chat history and state across sessions using a session ID.

Explore model routing with a fallback model and optional switch nodes, using Mistral Small as default, then test under real traffic by triggering concurrent workflows to assess performance and errors.

Implement redaction and filtering through upstream prevention and downstream anonymisation, using prompt engineering and privacy rules to detect PII, refuse unsafe queries, and perform pre-upload document cleaning with self-hosting.

Deploy an end-to-end meeting and call intelligence workflow that transcribes Google Drive audio with Assembly AI, summarizes with Mistral AI, and posts updates to Slack and Gmail.