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 models like Mixtral 8x7B (Mixture-of-Experts) and 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.
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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- Select and Route Models: Strategically choose and implement logic to route tasks between Mistral 7B and Mixtral 8x7B based on complexity and cost.
- Optimize Performance: Implement asynchronous and streaming API calls to achieve optimal latency and throughput.
- Enforce Structured Data: Master techniques and constraints to reliably force Mistral's output into strict, machine-readable formats (JSON, YAML).
- Design Tool Use: Build robust systems that utilize function calling to connect Mistral with internal databases and proprietary tools.
- Control Costs: Implement strategies for effective context management and caching to minimize token usage and production spend.
- Secure Code: Apply best practices for defending applications against common LLM vulnerabilities like prompt injection.
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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 and Architecture
Mistral's philosophy and core API structure (v1/chat/completions).
Deep Dive into Mixtral 8x7B (Mixture-of-Experts): Understanding the architecture's impact on speed, cost, and capacity.
Tokenization: Accurate token counting, context window management, and handling long inputs.
High-Performance Integration Patterns
Setting up the official Mistral SDK (Python/Node.js) and authentication.
Asynchronous Processing: Implementing async/await for high-throughput, non-blocking API calls.
Streaming Implementation: Building real-time user interfaces using streaming API responses to improve perceived latency.
Hands-on Lab: Implementing a resilient API wrapper with exponential backoff for error handling.
Prompt Engineering for Predictability
The three roles of chat: system, user, and assistant.
Mastering the System Prompt for defining task constraints, formatting rules, and model persona.
In-Context Learning: Using few-shot examples effectively to steer the model towards desired behavior.
Model Routing and Cost Control
The Cost Equation: Calculating token usage (input vs. output) for Mixtral vs. Mistral 7B.
Smart Routing Logic: Designing a gateway that intelligently routes simple classification or summarization tasks to the smaller, cheaper model and reserves the larger Mixtral for complex reasoning.
Implementing an aggressive caching layer for common queries to eliminate API calls and manage costs.
Structured Output Mastery
Enforcing Machine Readability: Advanced prompting techniques to reliably coerce Mistral's output into strict formats (JSON, XML).
Schema Validation: Integrating developer tools (like Pydantic in Python) for fast, reliable client-side validation of LLM outputs.
Error Recovery: Building code that automatically attempts to "repair" slightly malformed JSON or triggers a targeted re-prompt.
Hands-on Lab: Building a feature extraction endpoint that reliably returns data using a defined JSON schema.
Function Calling (Tool Use) Systems
Designing Effective Tools: Principles for defining functions (tools) with clear names, descriptions, and mandatory parameters.
The Full Cycle Architecture: Programming the logic where the model calls a tool, the developer code executes the tool, and the result is fed back to the model for final synthesis.
Error Handling in Tool Use: Managing exceptions that occur during tool execution and communicating them effectively back to the model.
Testing, Evaluation, and Security
Prompt Regression Testing: Creating and maintaining a Golden Test Set of inputs and desired outputs to automatically check fidelity against new prompt versions.
Mitigating Prompt Injection: Implementing strict separation and sanitization of user input to protect the system prompt.
Output Guardrails: Using post-processing checks to identify and redact sensitive or inappropriate content from model responses.
Deployment and Observability
Prompt Versioning: Creating a robust system for storing and managing prompt iterations alongside code in version control.
Observability Metrics: Tracking key production metrics (latency percentiles, tokens consumed, prompt error rates) to maintain operational health.
Deployment Strategy: Containerization best practices for scalable LLM microservices and efficient resource allocation.
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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/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/guides/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/guides/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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