Mistral AI Masterclass: Enterprise LLMs & RAG Systems

Explore the Mistral AI interface, including AI Studio, chat, agents, and the playground, with model selection, temperature, APIs, and deployment options.

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.

Prompt Mistral models to produce deterministic, business-ready outputs by enforcing structured json formats with explicit schemas and examples, using function calling, temperature control, and rigorous validation for production.

Build a production monitoring stack for Mistral that tracks token usage, costs, and performance. Deploy logging, tracing with OpenTelemetry, dashboards, alerts, and budget controls.

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.

Connect a backend app to Mistral AI by storing the API key as an environment variable and using the Mistral SDK to call chat complete with Mistral small latest model.

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.

Build a reliable Q&A interface with Streamlit to interact with a retrieval-based system in real time, displaying source citations and preventing hallucinations. No HTML or CSS required; write pure Python.

Deploy and secure an internal ai copilot by restricting network access, enforcing login, and storing keys as environment variables. Separate ingestion from user access and monitor usage.

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.

Learn to handle ambiguous and out-of-scope questions with agent flows, configuring a Mistral AI chat agent that asks for details, uses retrieved documents, and escalates to humans when needed.

Test a document-based customer support AI with in-scope and out-of-scope questions; save the agent flow, use the chat interface, and run 20–30 questions targeting 80–85% in-scope accuracy and proper redirection.

Connect the Mistral AI research agent to web search and external APIs using built-in tools, MCP, and an N8n workflow that automates queries and saves results to Google Sheets.

Convert messy AI outputs into a structured research report using a manual mapping workflow. Capture key fields such as timestamp, research topic, executive summary, market overview, trends, and recommendations.

Learn to schedule automated monitoring runs that proactively track markets, competitors, and trends, replacing manual triggers with a schedule-driven research workflow.

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.

Extract action items, decisions, summaries, and meeting metadata from transcripts using an agent node and customized prompts, then store structured outputs in a data table for targeted audience routing.

Publish an automated workflow that stores meeting metadata including action items, decisions, key topics, and next steps, using transcription services, with access control for live use and Google Drive triggers.

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.

Schedule daily content generation at 9am and publish approved posts to LinkedIn, using separate generation and publishing branches with human approval, then analyze performance to drive improvements.

Track the performance feedback loop for LinkedIn posts by extracting post URL, scraping engagement metrics after 24 hours, formatting as JSON, and logging to Google Sheets to inform prompt improvements.

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.

Identify sensitive data in an enterprise rag system by performing data mapping, auditing documents for PII and quasi-identifiers, and testing for echoed personal information to support EU GDPR compliance.

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.

Explore the EU AI Act risk-based framework, focusing on limited risk classification for a customer support chatbot, transparency, human oversight, data governance, and prompt injection testing.

Apply hardening for cost, security, and failure testing, including secure credential management with Docker, a dedicated error workflow, PII redaction options, and cost-aware use of Mistral AI and transcription services.

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.