10 lessons · 4hCertificate
AI for Software Engineers
From code completion to AI-powered products: the engineering skills that matter now
Free certificates — verified by employers
AI Courses for Software Engineers
You already use AI to write code. Now learn how to scope, integrate, and govern AI-powered features — and prove it with a credential.
Completely freeVerified certificatesNo account needed to start
Why Software Engineers Need AI Skills
The professional landscape is shifting. Here is what is at stake for software engineers who do not yet have a structured AI skills foundation.
Scoping AI product features requires a different skill set than building them
Writing code with AI assistance and scoping what an AI-powered feature should actually do are two distinct capabilities. Engineers who can define clear AI product requirements — including acceptable failure modes, latency tolerances, and evaluation criteria — are significantly more valuable on product teams than those who can only implement what they are handed.
Non-technical stakeholders need AI decisions explained without the jargon
Product managers, designers, and business stakeholders need to understand AI trade-offs — accuracy vs. speed, RAG vs. fine-tuning, rule-based vs. model-based — in terms they can act on. Engineers who can translate these decisions into clear business language are the ones who get heard in roadmap conversations.
Team-wide AI standards do not emerge naturally from individual tool use
Most engineering teams have individuals using AI tools in inconsistent, undocumented ways. Establishing shared prompting conventions, review standards for AI-generated code, and governance for AI-assisted architectural decisions requires a deliberate, structured approach — which is rarely the engineer's default mode.
Courses for Software Engineers
Free, self-paced courses ending in a verifiable certificate you can share on LinkedIn.
10 lessons · 5hCertificate
AI Security Engineering
AI systems have a completely different attack surface. This is what you need to know to defend them.
10 lessons · 5hCertificate
AI Testing and Reliability Engineering
You can't unit test a probabilistic system the same way. Here's how to build quality into LLM-powered products.
10 lessons · 5hCertificate
Building Production LLM Systems
From API call to production-grade LLM system: the engineering depth that separates demos from deployed products
Inside the Courses
A real excerpt of what each course covers, pulled straight from the lesson list.
AI for Software Engineers
- AI for Engineers: Beyond Code Completion
- How LLMs Work: An Engineering Mental Model
- The AI Tool Ecosystem: What Engineers Need to Know
- Scoping AI-Powered Product Features
- LLM Integration Patterns for Production Systems
- AI Product Requirements and Testing
+4 more lessons in the full course
AI Security Engineering
- The AI Security Threat Landscape
- Prompt Injection: Attacks and Defences
- Jailbreaks, Content Policy, and Model Abuse
- Data Poisoning and Training-Time Security
- Model Extraction, Inversion, and IP Protection
- PII Leakage, Data Privacy, and Confidential Information
+4 more lessons in the full course
AI Testing and Reliability Engineering
- Testing Non-Deterministic Systems: The Eval Mindset
- Designing Evaluation Frameworks for LLM Systems
- Evaluating RAG Systems and Factual Accuracy
- Regression Testing and Prompt Change Management
- CI/CD Pipelines for AI-Powered Features
- Load Testing and Latency Profiling for LLM Systems
+4 more lessons in the full course
Building Production LLM Systems
- RAG Architecture and Design
- Embedding Models and Retrieval System Design
- Structured Output and Production Validation
- Agent Patterns and Production Tool Use
- Prompt Caching and Cost Optimization at Scale
- LLM Evaluation Frameworks
+4 more lessons in the full course
Tools Covered
The specific tools taught inside these courses, referenced from the full tools directory.
- SQL
- Python
- Monday.com
- ChatGPT
- Claude
- Gemini
- Mistral Le Chat
- Qwen
- Llama (local)
- GitHub Copilot
- Cursor
- Aider
- GPT-4
- GitHub
- TypeScript
- Next.js
- Zod
- LangChain
- LlamaIndex
- Qdrant
- AWS Bedrock
- Vertex AI
- Azure OpenAI
- Together AI
- Replicate
- Vercel
- JetBrains IDEs
- CodeSandbox
- Confluence
- Slack
- Slack AI
- Redis
- Haystack
- Garak
- vLLM
- Hugging Face
- PyTorch
- spaCy
- Pinecone
- Weaviate
- Kubernetes
- Docker
- Datadog
- Grafana
- Prometheus
- HashiCorp Vault
- Trivy
- Syft
- Snyk
- Dependabot
- Shift Technology
- GitHub Actions
- PostgreSQL
- LangSmith
- Langfuse
- Braintrust
- PagerDuty
- OpenTelemetry
- LaunchDarkly
- Azure Document Intelligence
- DALL-E
- Whisper
- Stable Diffusion
- pgvector
- Jupyter
- LangGraph
- Helicone
- LiteLLM
- Ollama
Frequently Asked Questions
Common questions from software engineers considering these courses.
Is this course relevant if I already use GitHub Copilot and ChatGPT daily?
Especially relevant. The courses are designed to build the product scoping, strategic decision-making, and stakeholder communication capabilities that daily coding tool use does not develop. They complement your existing AI coding proficiency with the broader AI product and governance skills that determine career progression at senior levels.
Does AI Strategy cover LLM-specific decisions like RAG, fine-tuning, or model selection?
AI Strategy covers the decision frameworks for AI capability choices — including build vs. buy, evaluation criteria, and vendor assessment — at the level of strategic judgment rather than implementation detail. It equips engineers to participate in architecture decisions with product and business stakeholders, not to replace their existing technical depth.
How does AI for Business Analysis help a software engineer?
AI for Business Analysis teaches requirements gathering, process analysis, and stakeholder communication skills — applied in an AI context. For software engineers, this directly improves the ability to scope AI features, write clear acceptance criteria, and communicate AI system behaviour to non-technical team members.
Can I use these certificates to support a move into a tech lead or engineering manager role?
Yes. Tech lead and engineering manager roles increasingly require the ability to make and communicate AI architectural decisions, manage AI quality standards across a team, and engage credibly with product and business stakeholders. These certificates signal structured, tested competency in exactly those areas.
Top 5 AI Prompts for Software Engineers
Copy, adapt, and use these prompts directly in ChatGPT, Claude, or any major AI assistant.
Prompt 1
Code Review Explanation
Explain the following code change to a non-technical stakeholder: [CODE DIFF OR DESCRIPTION]. Cover: what the change does, why it was made, the trade-offs considered, and any risks to be aware of. Avoid jargon. Length: 150–200 words.Prompt 2
Technical Specification Draft
Write a technical specification for [FEATURE/SYSTEM]. Include: problem statement, proposed solution, key design decisions and alternatives considered, data model changes, API contract, error handling approach, and open questions. Audience: engineering team and tech lead.Prompt 3
Architecture Decision Record
Write an Architecture Decision Record (ADR) for the following decision: [DECISION]. Include: context and problem, decision drivers, options considered, decision outcome, consequences (positive and negative), and status.Prompt 4
Stakeholder Update
Write a weekly engineering update for [PROJECT/TEAM] for [PERIOD]. Audience: product manager and non-technical stakeholders. Cover: what shipped, what is in progress, blockers and how they are being resolved, and what is planned next week. Tone: clear and jargon-free.Prompt 5
Incident Post-Mortem
Write a post-mortem for the following incident: [INCIDENT DESCRIPTION]. Include: timeline, root cause analysis, contributing factors, impact summary, immediate actions taken, and corrective actions with owners and target dates. Tone: blameless and factual.Top 3 AI Tools for Software Engineers
The tools most used by software engineers who are already getting results with AI.
GitHub Copilot
AI code completion and chat integrated into VS Code and JetBrains — the standard AI coding companion for most engineers, now extended with workspace-aware chat and pull request summarization.
ChatGPT Plus
For technical specification drafting, architecture decision documentation, stakeholder communication, and post-mortem writing — GPT-4o handles technical prose and structured documentation well.
Cursor
AI-first code editor built on VS Code — tighter codebase awareness than Copilot, useful for refactoring complex systems, understanding unfamiliar codebases, and generating context-aware architectural suggestions.
What You'll Learn
A sample of the topics covered across the recommended courses for software engineers.
- 1AI capabilities and limitations for software engineering decisions
- 2Scoping AI-powered product features
- 3AI product requirements and acceptance criteria
- 4Communicating AI trade-offs to non-technical stakeholders
- 5Evaluating LLM integration patterns
- 6AI governance and quality standards for engineering teams
- 7Architecture decision-making in AI-integrated systems
- 8Building team-wide AI standards and documentation practices
Ready to get certified?
Every course on Deliberate Academy is free. No subscription, no credit card, no paywall. Read the lessons, pass the exam, and earn a certificate you can put on LinkedIn — today.