About the course
This two-day hands-on training course is built for software engineers transitioning from experimentation to building production-grade applications using the Mistral AI models. We will dissect the technical advantages of flagship models like Mistral Large 3 (utilizing advanced Mixture-of-Experts) and the latest Codestral releases, exploring how to leverage them for maximum speed and efficiency.
The program provides a deep technical focus on implementation patterns: mastering structured output (JSON generation), designing robust function calling systems, and implementing sophisticated strategies for model routing and cost control. You will leave with a clear, production-ready methodology for securing, testing, and deploying high-performance LLM features using the Mistral API.
The Mistral Methodology (Core Tenets)
Our training is built around three 2026 industry standards for AI-assisted engineering:
Human-in-the-Loop: Mistral is a co-pilot, not an autopilot. We focus on rigorous review patterns for agent-generated patches.
Local-First Architecture: Leveraging Ministral 3B (WebGPU) to save costs and protect IP, using Cloud APIs only for complex reasoning.
Model Distillation: Using Large flagship models to generate "Gold Datasets" to fine-tune smaller, faster local models.
Instructor-led online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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By the end of this course, attendees will be able to:
- Deploy Local LLMs: Run Ministral 3 and Codestral locally for zero-latency, private coding.
- Master Agentic Workflows: Use Devstral 2 and Mistral Vibe to automate multi-file refactors and bug fixes.
- Advanced Prompt Engineering: Use Chain-of-Thought (CoT) and System Instructions tailored for the Mistral Large 3 MoE architecture.
- Integration: Connect Mistral to your IDE (VS Code/JetBrains) and CI/CD pipelines.
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This hands-on workshop is designed for Software Engineers, ML Engineers, Technical Leads, and Solution Architects who are comfortable with coding and integrating external APIs, and whose primary goal is to build reliable, scalable AI features using the Mistral platform.
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Attendees must have strong proficiency in at least one modern programming language (Python is preferred for labs) and experience working with REST APIs.
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This Mistral AI course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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The Mistral Ecosystem & Setup
Model Selection: When to use Mistral Large 3 vs. Codestral vs. Ministral 3.
The Sovereign Stack: Setting up Mistral AI Studio vs. local deployment via Ollama or vLLM.
Privacy & Governance: Configuring on-premises environments for regulated industries.
Agentic Coding with Devstral & Vibe
Mistral Vibe CLI: Using the terminal-based agent to chat directly with your repository.
Multi-File Orchestration: Automating tasks that span multiple files and directories.
Tool Use & Function Calling: Teaching Mistral to use linters, compilers, and test runners to self-correct.
Advanced Prompting for Developers
Fill-In-The-Middle (FIM): Technical mastery of how Codestral handles code completion.
Context Management: Handling Mistral’s 256k context window without losing precision.
Structured Outputs: Forcing valid JSON, ASTs, or specific architectural patterns.
Maintenance & Legacy Modernization
Codebase Customization: Using Codestral Embed for semantic search across monorepos.
Refactoring Patterns: Identifying "code smells" and using subagents for architectural improvements.
Documentation: Auto-generating synchronized TSDoc/JSDoc and READMEs.
Testing, Security & CI/CD
Automated Test Generation: Writing unit tests for Vitest, Jest, or Playwright.
Vulnerability Scanning: Using specialized prompts to identify SQL injection or logic flaws.
CI/CD Integration: Running Mistral agents as "PR Reviewers" in GitHub Actions or GitLab CI.
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Core Mistral AI API and Documentation
Mistral AI Official API Documentation: The central reference for all API endpoints, including v1/chat/completions, authentication, and parameter specifics. https://docs.mistral.ai/api
Mistral Models Overview: Critical documentation detailing the features, context windows, and performance benchmarks for all available models, including Mixtral 8x7B (used for model routing strategies). https://docs.mistral.ai/getting-started/models
Official Mistral Python Client Library: Installation and usage instructions for the primary SDK used in the hands-on labs (Python is typically preferred for model interaction). https://pypi.org/project/mistralai/
Advanced Development and Tooling
Mistral Function Calling (Tool Use) Guide: Specific instructions and examples on how to define tools in the correct JSON schema for Mistral models to utilize, which is crucial for Module 6. https://docs.mistral.ai/capabilities/function_calling
Structured Output (JSON Mode) Implementation: Documentation showing how to enforce structured output for reliable machine readability, a core focus of Module 5. https://docs.mistral.ai/capabilities/structured_output
Mistral Tokenizer Tool: A utility or guide to accurately count tokens. This is vital for implementing the cost control strategies outlined in the Model Routing and Cost Control training module. (Note: While a dedicated public tokenizer tool URL is sometimes model-dependent, referencing the method or SDK implementation is key: https://docs.mistral.ai/api/#operation/getTokenizer)
Supplementary Engineering Best Practices
Pydantic Documentation: (External) The standard tool used in the Python ecosystem for defining robust data schemas and validating LLM-generated JSON, supporting Module 5's lab work. https://docs.pydantic.dev/
Mistral Blog for Engineering Case Studies: Often features articles and insights on latency reduction, model performance, and efficient deployment, offering real-world context for scaling. https://mistral.ai/news
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