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Course Outline
1. LLM Architecture and Core Techniques
- Comparison of Decoder-Only (GPT-style) and Encoder-Decoder (BERT-style) models.
- In-depth exploration of Multi-Head Self-Attention, positional encoding, and dynamic tokenization.
- Advanced sampling techniques: temperature adjustment, top-p selection, beam search, logit bias, and sequential penalties.
- Comparative analysis of leading models for government use: GPT-4o, Claude 3 Opus, Gemini 1.5 Flash, Mistral 8×22B, LLaMA 3 70B, and quantized edge variants.
2. Enterprise Prompt Engineering
- Layered prompt design: system prompt, context prompt, user prompt, and post-prompt processing for government applications.
- Techniques for Chain-of-Thought, ReACT, and auto-CoT with dynamic variables in governmental contexts.
- Structured prompt design using JSON schema, Markdown templates, and YAML function-calling for enhanced efficiency.
- Strategies to mitigate prompt injection: sanitization, length constraints, and fallback defaults to ensure security and reliability.
3. AI Tooling for Developers
- Overview and comparative analysis of GitHub Copilot, Gemini Code Assist, Claude SDKs, Cursor, and Cody for government use.
- Best practices for integrating these tools with IntelliJ (Scala) and VSCode (JavaScript/Python) to enhance development workflows.
- Cross-language benchmarking for coding, test generation, and refactoring tasks to optimize performance.
- Customizing prompts per tool: using aliases, contextual windows, and snippet reuse to improve efficiency.
4. API Integration and Orchestration
- Implementing OpenAI Function Calling, Gemini API Schemas, and Claude SDKs end-to-end for government applications.
- Managing rate limiting, error handling, retry logic, and billing metering to ensure smooth operation.
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Building language-specific wrappers:
- Scala: Akka HTTP
- Python: FastAPI
- Node.js/TypeScript: Express
- Utilizing LangChain components such as Memory, Chains, Agents, Tools, multi-turn conversation, and fallback chaining to enhance functionality.
5. Retrieval-Augmented Generation (RAG)
- Parsing technical documents in various formats: Markdown, PDF, Swagger, CSV using LangChain/LlamaIndex for government projects.
- Semantic segmentation and intelligent deduplication to improve data integrity.
- Working with embeddings: MiniLM, Instructor XL, OpenAI embeddings, Mistral local embedding for enhanced accuracy.
- Managing vector stores: Weaviate, Qdrant, ChromaDB, Pinecone—focusing on ranking and nearest-neighbor tuning to enhance search capabilities.
- Implementing low-confidence fallbacks to alternate LLMs or retrievers for robust performance.
6. Security, Privacy, and Deployment
- PII masking, prompt contamination control, context sanitization, and token encryption to ensure data security in government applications.
- Prompt/output tracing: creating audit trails and unique IDs for each LLM call to maintain accountability.
- Setting up self-hosted LLM servers (Ollama + Mistral), optimizing GPU usage, and implementing 4-bit/8-bit quantization for efficient resource utilization in government environments.
- Kubernetes-based deployment: using Helm charts, autoscaling, and warm start optimization to ensure reliable service delivery.
Hands-On Labs
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Prompt-Based JavaScript Refactoring
- Multi-step prompting process: detect code smells → propose refactor → generate unit tests → inline documentation for enhanced code quality.
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Scala Test Generation
- Property-based test creation using Copilot vs Claude; measuring coverage and edge-case generation to improve testing efficiency.
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AI Microservice Wrapper
- REST endpoint that accepts prompts, forwards to LLM via function-calling, logs results, and manages fallback logic for seamless integration.
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Full RAG Pipeline
- Simulated documents → indexing → embedding → retrieval → search interface with ranking metrics to optimize document management.
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Multi-Model Deployment
- Containerized setup with Claude as the primary model and Ollama as a quantized fallback; monitoring via Grafana with alert thresholds for continuous performance tracking.
Deliverables
- Shared Git repository containing code samples, wrappers, and prompt tests for government use.
- Benchmark report detailing latency, token cost, and coverage metrics to inform decision-making.
- Preconfigured Grafana dashboard for monitoring LLM interactions in real-time.
- Comprehensive technical PDF documentation and versioned prompt library to support ongoing development and maintenance.
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 for government projects.
- Basic understanding of Docker and Kubernetes concepts.
- Interest in applying AI/LLM technologies to enterprise software engineering initiatives.
Audience
- Software Engineers and AI Developers for government agencies
- Technical Architects and Solution Designers in public sector organizations
- DevOps Engineers implementing AI pipelines for government systems
- R&D teams exploring AI-assisted development for government applications
35 Hours
Testimonials (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session
