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Course Outline
1. LLM Architecture and Core Techniques
- Comparison between Decoder-Only (GPT-style) and Encoder-Decoder (BERT-style) models.
- Deep dive into Multi-Head Self-Attention, positional encoding, and dynamic tokenization.
- Advanced sampling: temperature, top-p, beam search, logit bias, and sequential penalties.
- Comparative analysis of leading models: GPT-4o, Claude 3 Opus, Gemini 1.5 Flash, Mistral 8×22B, LLaMA 3 70B, and quantized edge variants.
2. Enterprise Prompt Engineering
- Prompt layering: system prompt, context prompt, user prompt, and post-prompt processing.
- Techniques for Chain-of-Thought, ReACT, and auto-CoT with dynamic variables.
- Structured prompt design: JSON schema, Markdown templates, YAML function-calling.
- Prompt injection mitigation strategies: sanitization, length constraints, fallback defaults.
3. AI Tooling for Developers
- Overview and comparative use of GitHub Copilot, Gemini Code Assist, Claude SDKs, Cursor, and Cody.
- Best practices for IntelliJ (Scala) and VSCode (JS/Python) integration.
- Cross-language benchmarking for coding, test generation, and refactoring tasks.
- Prompt customization per tool: aliases, contextual windows, snippet reuse.
4. API Integration and Orchestration
- Implementing OpenAI Function Calling, Gemini API Schemas, and Claude SDK end-to-end.
- Handling rate limiting, error management, retry logic, and billing metering.
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Building language-specific wrappers:
- Scala: Akka HTTP
- Python: FastAPI
- Node.js/TypeScript: Express
- LangChain components: Memory, Chains, Agents, Tools, multi-turn conversation, and fallback chaining.
5. Retrieval-Augmented Generation (RAG)
- Parsing technical documents: Markdown, PDF, Swagger, CSV with LangChain/LlamaIndex.
- Semantic segmentation and intelligent deduplication.
- Working with embeddings: MiniLM, Instructor XL, OpenAI embeddings, Mistral local embedding.
- Managing vector stores: Weaviate, Qdrant, ChromaDB, Pinecone – ranking and nearest-neighbor tuning.
- Implementing low-confidence fallbacks to alternate LLMs or retrievers.
6. Security, Privacy, and Deployment
- PII masking, prompt contamination control, context sanitization, and token encryption.
- Prompt/output tracing: audit trails and unique IDs for each LLM call.
- Setting up self-hosted LLM servers (Ollama + Mistral), GPU optimization, and 4-bit/8-bit quantization.
- Kubernetes-based deployment: Helm charts, autoscaling, and warm start optimization.
Hands-On Labs
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Prompt-Based JavaScript Refactoring
- Multi-step prompting: detect code smells → propose refactor → generate unit tests → inline documentation.
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Scala Test Generation
- Property-based test creation using Copilot vs Claude; measure coverage and edge-case generation.
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AI Microservice Wrapper
- REST endpoint that accepts prompts, forwards to LLM via function-calling, logs results, and manages fallback logic.
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Full RAG Pipeline
- Simulated documents → indexing → embedding → retrieval → search interface with ranking metrics.
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Multi-Model Deployment
- Containerized setup with Claude as main model and Ollama as quantized fallback; monitoring via Grafana with alert thresholds.
Deliverables
- Shared Git repository containing code samples, wrappers, and prompt tests.
- Benchmark report: latency, token cost, coverage metrics.
- Preconfigured Grafana dashboard for LLM interaction monitoring.
- Comprehensive technical PDF documentation and versioned prompt library.
Troubleshooting
Summary and Next Steps
Requirements
- Familiarity with at least one programming language (Scala, Python, or JavaScript).
- Knowledge of Git, REST API design, and CI/CD workflows.
- Basic understanding of Docker and Kubernetes concepts.
- Interest in applying AI/LLM technologies to enterprise software engineering.
Audience
- Software Engineers and AI Developers
- Technical Architects and Solution Designers
- DevOps Engineers implementing AI pipelines
- R&D teams exploring AI-assisted development
35 Hours
Testimonials (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session
