Name: Prompt Engineering for Software Development (Devin)
Availability: InStock

Prompt Engineering for Software Development (Devin)

An advanced, practice-driven course for developers who already use AI coding tools. The focus is on working faster and safer by mastering prompt patterns, agent workflows, and integration techniques. The course centers on Devin as a primary agentic tool with real repositories and realistic tasks.

What will you learn?

You will refine how you scope tasks, design prompts, and steer agentic tools to deliver reliable code changes. You will connect agent workflows to your repos, tests, and CI, introduce retrieval and tool use where it helps, and establish evaluation and governance practices that scale across a team.

After this training you will be confident in:

• Framing software tasks for agentic execution and choosing the right prompting strategies

• Steering Devin to read, modify, and propose changes across multi-file codebases

• Grounding generations with repository context, documentation, and structured retrieval

• Generating, refining, and maintaining tests as control points for agent iterations

• Integrating prompts and agents into Git, issue trackers, and CI to reduce manual steps

• Evaluating quality with metrics, traces, and guardrails while managing privacy and IP risk

Requirements:

• Strong command of Git, branching, and code review workflows

• Familiarity with CI systems and package managers in at least one language

• Access to a non-sensitive repository and an agentic coding tool account

• Optional but helpful: container runtime and a small service or CLI project to use during the course

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

Module 1: Agentic coding for professionals

Where agent tools fit in the SDLC and when not to use them
Capabilities and limits of agentic systems vs code assistants
Reading traces and understanding tool invocation patterns

Module 2: Devin orientation and setup for real projects

Connecting to a repository, installing dependencies, and environment prep
Project scoping and task decomposition for multi-step work
Prompt scaffolds for goals, constraints, acceptance criteria, and deliverables

Module 3: High-signal prompts for code changes

Change requests as specs: intent, invariants, and impact surface
Context packing: files, symbols, logs, configs, and decisions the model must respect
Iterative prompting for edits, explanations, and diffs

Module 4: Grounding with documentation and retrieval

When to use retrieval and how to select high-value sources
Patterns for linking design docs, ADRs, and API references
Keeping context windows lean while preserving critical constraints

Module 5: Tests as guardrails

Generating and tightening unit and integration tests before code edits
Golden files, fixtures, and property-based checks as stability anchors
Using failing tests to steer subsequent prompts and reduce regressions

Module 6: Debugging and refactoring with agents

Failure triage prompts and log-driven diagnosis
Safe refactoring prompts that preserve public contracts
Producing human-grade commit messages and change notes

Module 7: Tool use and extension

Selecting tools the agent should call: linters, formatters, docs generators, test runners
Command constraints, rate limits, and timeout strategies
Handling long-running tasks and chunked objectives

Module 8: CI, issue trackers, and review culture

Branching strategies that complement agent workflows
Connecting prompts to issues, tickets, and PR templates
Review checklists and two-pass approvals for agent-authored changes

Module 9: Performance tuning for reliability and speed

Prompt brevity vs completeness and when to switch strategies
Few-shot and chain-of-thought alternatives without leaking secrets
Measuring cycle time, rework, and pass rates to improve prompts

Module 10: Security, privacy, and IP management

Redacting secrets and minimizing data exposure
License awareness and provenance considerations for generated code
Audit trails, decision logs, and disclosure practices

Module 11: Explainability and stakeholder communication

Producing concise rationales, design notes, and risk callouts
Translating model traces into developer-readable narratives
Capturing decisions for future maintainers

Module 12: From pilot to team adoption

Prompt libraries, templates, and conventions that scale
Playbooks for common tasks such as feature scaffolding, bug fixes, and migrations
Roadmap for expanding agent coverage while keeping humans in control

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Hands-on learning with expert instructors at your location for organizations.

3.072€*