🖥️
Full-Stack & Backend Engineers

AI for Software Engineers

Stop using AI as autocomplete. Start shipping AI features to production.

For practising software engineers who want to go beyond GitHub Copilot. Covers LLM APIs, RAG integration, AI agents, and MCP — all within the software delivery lifecycle you already know.

Duration
8 weeks · 6hrs/week
Format
Live online + async labs with HYVE engineer code review
Prerequisite
Comfortable with at least one backend language

Curriculum

01

LLM APIs in Production

6hrs

Auth, rate limiting, cost management, streaming, error handling, fallback strategies across OpenAI, Anthropic, and Google APIs.

Lab: Build a robust LLM wrapper with retry, cost tracking, and automatic fallback.
02

Advanced Prompt Engineering

6hrs

System prompts, structured outputs, JSON mode, function calling, prompt versioning and A/B testing at scale.

Lab: Build a prompt versioning system with A/B testing and accuracy tracking.
03

RAG Systems in Production

8hrs

Vector DB selection (Pinecone, pgvector, Chroma), embeddings, chunking, hybrid search, re-ranking — production-ready patterns.

Lab: Add RAG-powered search to a REST API — under 200ms latency target.
04

AI Feature Patterns & Anti-patterns

6hrs

When to use AI vs traditional code. Latency budgets, caching, feature flags, graceful degradation.

Lab: Refactor a chatbot feature with proper caching, fallback, and monitoring.
05

AI Agents for Engineers

8hrs

Agent architecture, tool-use, function calling, memory systems, making agents reliable in production.

Lab: Build an agent that triages support tickets and queries your product database.
06

MCP: Connect AI to Your Systems

6hrs

Build MCP servers exposing your app's data and actions to AI agents securely. HYVE production patterns included.

Lab: Build an MCP server exposing your database and API to a Claude agent.
07

Testing & Evaluating AI

6hrs

Testing non-deterministic outputs, LLM evaluation frameworks, regression testing, and monitoring AI in production.

Lab: Build an evaluation suite for a RAG feature with automated regression tests.
08

Shipping AI to Production

6hrs

Deployment patterns, observability, cost dashboards, alerting — how HYVE manages UAE enterprise AI at scale.

Lab: Deploy an AI API with full observability — latency, cost, accuracy dashboards.

Learning Outcomes

  • ✓Integrate LLM APIs into existing products
  • ✓Build & deploy RAG search features
  • ✓Create AI agents with tool-use and memory
  • ✓Expose enterprise data to AI via MCP
  • ✓Evaluate LLM performance and manage costs

FAQs