Rust for Interfacing with Language Models (LFWS309)

Training > AI/Machine Learning > Rust for Interfacing with Language Models (LFWS309)

INSTRUCTOR-LED COURSE

Advance into high-impact AI engineering roles by learning to build reliable, production-grade LLM applications in Rust. Design type-safe clients, structured outputs, streaming interfaces, RAG pipelines, and agents using the PAIML stack, skills teams need as AI systems move from prototype to production.

Who Is It For

For Rust developers, backend engineers, and platform engineers advancing into AI application and platform roles. Ideal for professionals who rely on Rust’s predictability and performance to deliver dependable LLM applications with structured outputs, streaming, RAG systems, and agents.

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What You’ll Learn

Gain the skills to build and operate production-grade LLM systems in Rust. Learn to design type-safe integrations, manage structured outputs and streaming responses, orchestrate tools, and implement RAG pipelines and autonomous agents using the PAIML stack.

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What It Prepares You For

Advanced roles such as AI Application Engineer and LLM Platform Engineer. This course equips professionals to design and operate production LLM systems in Rust, supporting advanced agents, next-generation copilots, and reliable AI workflows across integration, streaming, RAG pipelines, and tool orchestration.

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Course Outline

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Course Introduction

LLM API Clients in Rust

Lab 2.1. Build unified client calling OpenAI, Anthropic, and local llamafile. Implement automatic failover between providers.

Structured Output Parsing

Lab 3.1. Extract structured data from unstructured text. Define Person { name, age, occupation } schema. Handle malformed LLM outputs gracefully.

Streaming Responses

Lab 4.1. Build streaming CLI chat. Display tokens as received. Handle connection drops and timeouts. Measure time-to-first-token.

Multi-Turn Conversations

Lab 5.1. Build conversation manager with 8K token window. Auto-summarize when exceeding limit. Persist and restore sessions.

Tool Use and Function Calling

Lab 6.1. Implement tools: calculator, web_search, file_read. Build assistant that selects and chains tools to answer queries.

Embeddings with Hugging Face

Lab 7.1. Benchmark three HF embedding models on semantic similarity task. Measure throughput and quality tradeoffs.

RAG Pipelines with Pacha

Lab 8.1. Build RAG over documentation. Index, retrieve top-3 chunks, generate grounded answer with citations.

Building Agents

Lab 9.1. Build research agent: takes question, plans search strategy, executes web searches, synthesizes answer. Limit to 5 tool calls.

Capstone: CLI Assistant

Course Summary

Prerequisites

Knowledge/Skills Prerequisites:

To get the most from this course, you need solid Rust proficiency including async/await patterns, serde for serialization, and practical error handling with Result and anyhow. You should also understand HTTP/REST API concepts such as endpoints, request/response formats, and status codes, plus a basic grasp of large language models including prompts, tokens, and completions.

Lab Environment Prerequisites: