DevOps & AI Fluency - Integrating Generative AI into the SDLC

Technical Training in Generative AI for DevOps Teams

This is not traditional training. It’s an accompaniment program (including reverse KT sessions, hands-on workshops and a handful of AI examples we built at Lab34) designed for technical teams to learn by doing and become fluent professionals in using generative AI throughout the entire software development lifecycle (SDLC).

We start from deep technical fundamentals—the “physics” of LLMs—and then apply them culturally in every phase of the DevOps process, from planning to observability.

Program Structure

The program evolves from deep technical understanding to practical and cultural application in every phase of the DevOps cycle.

Module 1: Technical Fundamentals – The “Physics” of LLMs

Objective: Demystify AI. Understand technical constraints (memory, costs, input/output) to design robust, not “magical” solutions.

1.1 Anatomy of an LLM: Tokens vs. Words

Token concept and tokenization differences between natural language and code
DevOps implications: API cost calculation and optimization
Practical exercise: “The token calculator” - Estimate consumption in Kubernetes configuration files

1.2 The Context Window: The “RAM” of AI

Short-term memory: Input + Output tokens = Total limit
The “Lost in the Middle” phenomenon in long contexts
DevOps implications: Chunking strategies and basic RAG
Why you can’t “copy and paste” a 1GB log

1.3 Inference Parameters: Controlling the Chaos

Temperature: Determinism vs. Creativity in different contexts
Probability and Hallucination: Why the model invents non-existent libraries
Understanding that it predicts tokens, not absolute truths

Module 2: DevOps Culture and AI Strategy

Objective: Align technical capabilities with DevOps collaboration and trust philosophy.

2.1 The Three Modes of Engineering Interaction

Automation: Migration scripts, seed data generation
Augmentation: “Pair Programming” to solve complex incidents
Agency: Bots that triage Jira tickets or do pre-code reviews

2.2 The 4 D Framework Applied to DevOps

Delegation: Decision matrix - What task is safe for AI?
Description: Prompting as a fundamental new technical skill
Discernment: “Trust but Verify” - AI as junior developer
Diligence: Ethics, biases, and intellectual property of generated code

Module 3: Planning and Architecture Design

Objective: Use AI to reduce friction in the design phase and foster shared ownership.

3.1 From Requirements to Specifications

Translate vague User Stories into detailed technical specifications
Generate Mermaid diagrams and Gherkin specifications

3.2 Platform Design and RFCs

Overcome “writer’s block” when drafting RFCs (Request for Comments)
Role simulation: “Act as a security expert and critique this architecture”
Exercise: Collaborative architecture design with AI critique

Module 4: Development and Test Automation (CI)

Objective: Accelerate feedback cycle (“Shift Left”) through better code and early testing.

4.1 Prompt Engineering for Code

Chain-of-Thought: Guide AI step by step
Few-Shot Prompting: Teach team style for consistency
Maintain repository coherence

4.2 Test and Data Generation

Generate unit and integration tests
Edge cases that humans often forget
Synthetic data and database mocks

Module 5: Infrastructure and Pipelines (CD & IaC)

Objective: Infrastructure automation and reliable deployments.

5.1 Infrastructure as Code (IaC)

Intention to code translation: From requirements to Terraform/CloudFormation
Script auditing: Detect security errors before apply
Exercise: Generate and validate infrastructure configurations

5.2 CI/CD Scripts and Automation

Generate optimized pipelines (GitHub Actions, GitLab CI)
Modernize Legacy scripts: From old Bash to Python/Go
Optimize build times

Module 6: Observability, Security, and Governance

Objective: Close the loop with constant feedback and secure operations.

6.1 Incident and Log Analysis

Efficient use of context window to analyze error traces
Write blameless Post-Mortems
Summarize complex incident timelines

6.2 Security (DevSecOps)

Vulnerability scanning in generated code
The risk of exposing secrets (API Keys) to public LLMs
Best security practices with AI

6.3 Governance and Future

Company AI usage policy
Preparation for Autonomous Agents in Operations
The future of software development with AI

Methodology

Hands-on learning: Practical exercises in each module
Based on real cases: Examples from day-to-day DevOps team work
Fluency over Knowledge: The goal is fluency, not just theoretical knowledge
Continuous accompaniment: Support beyond formal sessions
Adapted to your stack: Examples customized to your team’s technologies

Who Is This Program For?

DevOps/SRE engineering teams
Developers wanting to integrate AI into their workflow
Technical Leads seeking strategic AI implementation
Platform teams wanting to automate with intelligence

Expected Outcomes

Upon completing the program, your team will be able to:

Deeply understand how LLMs work and their limitations
Effectively integrate generative AI at every SDLC phase
Design precise technical prompts and get predictable results
Critically evaluate code and suggestions generated by AI
Implement governance and security policies for AI use
Accelerate development without compromising quality
Become AI-fluent professionals

Contact

Interested in taking your team to the next level in generative AI? Let’s discuss how to adapt this program to your organization’s specific needs.