BR Academy

Prompt Engineering and LLM Apps in Production: Evals, Cost and Safety

Ship production LLM features with evals, cost control, and safety guardrails. 24 lessons + capstone.

Intermediate31 lessons8.8 hoursCertificate

About this course

You’ve used an LLM API. Now ship it to production.

Most engineers treat LLM features like prototypes: prompt-tweak-and-pray, no evals, no cost tracking, no safety guardrails. That works until it doesn’t—until your prompt drifts, your costs spike, or your system fails in production.

This course teaches you how to build LLM systems that are measurable, cost-controlled, safe, and debuggable.

You’ll learn:
– **Prompt design patterns** that actually work: few-shot learning, chain-of-thought, role-play, and how to test them
– **Structured outputs and tool use** so your LLM’s responses are reliable and parseable
– **Retrieval systems (RAG)** that work: chunking, embedding, ranking, and measuring quality
– **Evals and regression suites** that catch drift before production
– **Cost and latency budgeting** so you stay within your SLAs
– **Caching, batching, and optimization** to squeeze every token
– **Guardrails and red-teaming** to ship safely
– **Observability and monitoring** so you know when things break

Then you’ll ship a complete LLM feature end to end: design, build, test, optimize, deploy, monitor. You’ll write a postmortem of a failure and how you fixed it. That’s your capstone.

This is enterprise-grade training. Every lesson teaches one idea with a worked example and a hands-on exercise. No filler. No marketing. Just the patterns and practices that separate hobby projects from production systems.

By the end, you’ll have built and shipped a real LLM feature. You’ll know how to measure it, optimize it, and keep it safe. You’ll have a certificate and a portfolio project. And you’ll be ready to ship LLM features at scale.

**Who this is for:**
Backend and full-stack engineers shipping LLM features. You know Python and APIs. You’ve called an LLM API before. You’re ready to move beyond prompt tweaking.

**What you’ll build:**
A complete LLM feature: prompt + retrieval + structured outputs + evals + cost tracking + guardrails + observability. You’ll ship it and write a postmortem.

**Questions?** Contact us at https://blockrelations.com/contact/

What you will be able to do

Can design and test prompt patterns (few-shot, chain-of-thought, role-play) using structured evaluation frameworks
Can implement structured outputs and tool use to make LLM responses reliable and parseable
Can build and run a retrieval system (RAG) that actually works: chunking, embedding, ranking, and reranking
Can write evals and regression suites that catch prompt drift and model changes before production
Can measure, budget, and optimize token costs and latency in production workloads
Can implement caching, batching, and request coalescing to stay within cost and latency SLAs
Can add guardrails, red-team your own system, and monitor for safety issues in production
Can ship a complete LLM feature end to end with observability, testing, and rollback

Curriculum 7 modules · 31 lessons

1Prompt Design and Patterns5 lessons

Prompt Basics and Mental Models Preview8 min
Few-Shot Learning and In-Context Examples 12 min
Chain-of-Thought and Reasoning Patterns 12 min
Role-Play and Persona Prompting 10 min
Testing Prompts with Structured Evals 15 min

2Structured Outputs and Tool Use5 lessons

Structured JSON Outputs and Parsing 12 min
Pydantic Models and Runtime Validation 12 min
Tool Use and Function Calling 15 min
Multi-Tool Routing and Agentic Loops 15 min
Hands-On Lab: Build a Multi-Tool Agent 20 min

3Retrieval Systems (RAG)5 lessons

RAG Fundamentals and Architecture 12 min
Chunking Strategies and Embedding Selection 15 min
Retrieval, Ranking, and Reranking 15 min
Measuring RAG Quality: Precision, Recall, NDCG 12 min
Hands-On Lab: Build and Evaluate a RAG System 25 min

4Evals and Regression Testing5 lessons

Eval Frameworks and Metrics 15 min
LLM-as-Judge and Semantic Scoring 15 min
Building Regression Test Suites 15 min
Detecting Prompt Drift and Model Changes 12 min
Hands-On Lab: Write Evals for Your Own System 20 min

5Cost, Latency, and Optimization5 lessons

Token Costs and Budget Planning 12 min
Latency SLOs and Tail Percentiles 12 min
Prompt Caching and KV Cache 15 min
Batching and Request Coalescing 15 min
Hands-On Lab: Optimize Cost and Latency 20 min

6Safety, Guardrails, and Monitoring5 lessons

Guardrails and Input/Output Filtering 15 min
Red-Teaming Your Own LLM System 15 min
Detecting Jailbreaks and Adversarial Inputs 12 min
Monitoring and Alerting in Production 15 min
Hands-On Lab: Add Guardrails and Monitor 20 min

7Capstone Project1 lesson

Ship an LLM Feature End to End 90 min

Taught by Block Relations

Research & Education · Built for backend or full-stack engineers shipping LLM features into production systems. You know Python and APIs, and you've used an LLM API before. You need to move beyond prompt tweaking and build systems that are measurable, cost-controlled, safe, and debuggable when they fail.