AI Training for Software Developers
AI and GenAI training for software engineers, DevOps teams, and tech leads — covering LLM API integration, RAG pipeline design, prompt engineering for code, MLOps, and AI-assisted development with Copilot and Claude.
Every developer team is now expected to build AI features, evaluate AI tools, and maintain AI-assisted codebases. The gap is not just knowing that LLMs exist — it is knowing how to integrate them reliably, manage their failure modes in production, and build RAG pipelines that actually work on your organisation's data. This training bridges that gap with hands-on labs on real engineering problems.
Challenges AI Solves in Software Developers
Our programmes are built around the real operational pressures your teams face every day.
Teams asked to ship AI features with no foundational training
Product teams are expected to add GenAI functionality without any formal grounding in how LLMs work, when they fail, or how to build reliable pipelines around them. Shortcuts taken here become production incidents later.
AI coding tools used inefficiently — or not at all
GitHub Copilot, Claude Code, and ChatGPT can cut development time by 30–50% for teams that use them well. Most teams never get past basic autocomplete because they haven't learned how to prompt for engineering tasks or review AI-generated code critically.
RAG and LLM pipelines failing silently in production
Hallucination, context window overflow, retrieval quality issues, and prompt injection are not edge cases — they are common failure modes. Developers need to understand these patterns and how to build evaluation and monitoring into their systems.
AI Use Cases We Train Your Team On
Every lab exercise maps to a real scenario your teams will encounter in their role.
LLM API Integration
Integrate OpenAI, Anthropic Claude, and Azure OpenAI APIs into applications. Streaming responses, function calling, structured output, error handling, and cost management — all in production-grade patterns.
RAG Pipeline Design
Build retrieval-augmented generation systems end-to-end: document chunking strategies, embedding model selection, vector database setup (Pinecone, Chroma, pgvector), retrieval evaluation, and re-ranking.
Prompt Engineering for Developers
Advanced prompting techniques for code generation, code review, test generation, and documentation. Chain-of-thought, few-shot, and system prompt design for consistent output quality.
AI-Assisted Development with Copilot
Master GitHub Copilot and Claude Code for real engineering tasks: refactoring legacy code, writing unit tests, generating boilerplate, reviewing diffs, and explaining unfamiliar codebases.
MLOps & AI Model Deployment
Deploy ML models and LLM applications: containerisation with Docker, CI/CD pipelines for ML, monitoring model drift, logging and observability for AI systems, and rollback strategies.
AI Security & Production Reliability
Prompt injection, jailbreaking defence, PII leakage prevention, output validation, rate limiting, and cost guardrails — essential knowledge for any team shipping AI to end users.
Recommended Programmes for Software Developers
Each programme can be customised with your branding, data, and industry examples.
Data Science & ML Training
Developers building ML-powered features and data pipelines.
View curriculum →Prompt Engineering Mastery
Developers integrating LLMs and building GenAI features.
View curriculum →AWS Cloud Practitioner
Developers deploying AI workloads on AWS SageMaker, Bedrock, or Lambda.
View curriculum →Microsoft Azure Fundamentals
Developers using Azure OpenAI, Cognitive Services, or Azure ML.
View curriculum →Google Cloud (GCP) Training
Developers working with Vertex AI, BigQuery ML, or GCP services.
View curriculum →Machine Learning for Non-Technical Teams
Tech leads and architects who need ML system design literacy.
View curriculum →What Your Team Will Be Able to Do
Measurable outcomes your L&D team can report on.
What Participants Say
“The RAG pipeline lab was exactly what we needed. We had been hacking around retrieval quality issues for weeks. After the training, our engineering team rebuilt it properly in two days — retrieval precision went from 60% to 91%.”
Vikram Singh
Staff Engineer, SaaS Product Company, Bangalore
“The Copilot module alone justified the training cost. We tracked the time savings — our team went from roughly 40% AI tool adoption to 85% in the month after training. Measurable impact on velocity.”
Ananya Krishnan
Engineering Manager, FinTech Startup, Mumbai
“We had shipped a GenAI feature without thinking about prompt injection or PII leakage. The security module in this training found three issues in our existing production code on day 1.”
Rahul Desai
Senior Backend Developer, IT Services Firm, Hyderabad
Why Corporate Teams Choose Technovids
On-site at Your Office
We come to you with all lab equipment. No co-ordination overhead for your team.
Software Developers-Specific Content
Labs and case studies drawn from your sector. No generic tech-company examples.
Practitioner Trainers
8–15 years of real-world experience. Not career trainers — working engineers and architects.
Verifiable Certificates
LinkedIn-shareable certificates issued within 48 hours of programme completion.
30-Day Support
Post-training WhatsApp group with your trainer. Questions answered, not forgotten.
Measurable Outcomes
Pre/post assessment + manager's report showing exactly what improved.
Frequently Asked Questions
What programming languages do the labs use?
Labs are primarily Python-based (industry standard for AI/ML integration), with JavaScript/TypeScript examples for front-end LLM integration patterns. If your team works primarily in another language (Java, C#, Go), we can adapt the API integration labs accordingly.
Do participants need ML or data science knowledge before attending?
No. The developer track focuses on LLM API integration, RAG systems, and AI-assisted development — not building models from scratch. Developers with strong software engineering skills but no ML background can engage fully with all labs.
Can you cover our specific cloud stack — AWS Bedrock, Azure OpenAI, or GCP Vertex AI?
Yes. We customise the cloud deployment and LLM API labs to your organisation's preferred cloud and AI service layer. We cover the same RAG and integration patterns but using your team's actual tools.
Is there a module specifically on AI code review and quality?
Yes. We cover AI-generated code quality: common patterns where Copilot and Claude produce incorrect or insecure code, how to review AI output effectively, and how to build automated checks (linting, testing, security scanning) into AI-assisted development workflows.
Can we run this as a hackathon-style session rather than a structured course?
Yes. For senior engineering teams, we offer a build-day format: a half-day of structured foundations followed by a guided build session where teams ship a working AI feature — RAG system, Copilot-accelerated refactor, or LLM integration — by end of day.
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