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Course Outline
1. LLM Architecture and Core Techniques
- Evaluation of Decoder-Only architectures (GPT-style) versus Encoder-Decoder models (BERT-style) for federal data processing requirements.
- Analysis of Multi-Head Self-Attention mechanisms, positional encoding strategies, and dynamic tokenization processes to ensure accurate information retrieval. Advanced sampling methodologies including temperature control, top-p filtering, beam search, logit bias application, and sequential penalty adjustments. Comparative assessment of leading foundational models—GPT-4o, Claude 3 Opus, Gemini 1.5 Flash, Mistral 8×22B, LLaMA 3 70B, and quantized edge variants—specifically for government use cases.
2. Enterprise Prompt Engineering
- Implementation of prompt layering strategies, encompassing system directives, contextual inputs, user queries, and post-prompt processing protocols.
- Application of Chain-of-Thought reasoning, ReACT frameworks, and automated CoT techniques utilizing dynamic variables for complex decision-making support. Structured prompt design standards using JSON schemas, Markdown templates, and YAML function-calling specifications to ensure data integrity. Strategies for mitigating prompt injection risks through input sanitization, length constraints, and predefined fallback defaults.
3. AI Tooling for Developers
- Assessment of developer assistance tools including GitHub Copilot, Gemini Code Assist, Claude SDKs, Cursor, and Cody in the context of secure software development life cycles.
- Best practices for integrating AI capabilities within IntelliJ (Scala) and VSCode (JS/Python) environments to enhance code quality and consistency. Cross-language benchmarking focused on coding efficiency, automated test generation, and refactoring tasks for federal IT modernization efforts. Customization of prompt configurations per tool, including alias management, contextual window sizing, and snippet reuse protocols to streamline workflow.
4. API Integration and Orchestration
- Implementation of OpenAI Function Calling, Gemini API schemas, and Claude SDK end-to-end integrations for interoperable government systems.
- Management of rate limiting, error handling protocols, retry logic, and billing metering to ensure cost-effective operation of AI services. Development of language-specific API wrappers:
- Scala: Akka HTTP implementations
- Python: FastAPI-based services
- Node.js/TypeScript: Express framework integration
5. Retrieval-Augmented Generation (RAG)
- Processing of technical documentation formats such as Markdown, PDF, Swagger specifications, and CSV files using LangChain and LlamaIndex frameworks.
- Execution of semantic segmentation and intelligent deduplication algorithms to optimize information relevance and reduce redundancy. Application of embedding models including MiniLM, Instructor XL, OpenAI embeddings, and local Mistral embeddings for precise vector representation. Management of vector databases such as Weaviate, Qdrant, ChromaDB, and Pinecone, with tuning for ranking and nearest-neighbor search efficiency. Implementation of low-confidence result fallbacks to alternative LLM providers or retrievers to maintain service continuity.
6. Security, Privacy, and Deployment
- Protocols for PII masking, prompt contamination control, context sanitization, and token encryption to protect sensitive citizen data.
- Establishment of prompt and output tracing mechanisms, including comprehensive audit trails and unique identifiers for every LLM interaction. Configuration of self-hosted LLM servers using Ollama and Mistral, including GPU optimization techniques and 4-bit/8-bit quantization for resource efficiency. Kubernetes-based deployment strategies utilizing Helm charts, autoscaling policies, and warm start optimizations to ensure high availability and compliance.
Hands-On Labs
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Prompt-Based JavaScript Refactoring
- Multi-step prompting workflow: detection of code smells → proposal of refactoring strategies → generation of unit tests → integration of inline documentation.
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Scala Test Generation
- Creation of property-based tests using GitHub Copilot versus Claude; measurement of code coverage and edge-case identification capabilities.
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AI Microservice Wrapper Development
- Construction of a REST endpoint that accepts prompts, routes them to an LLM via function-calling, logs results for auditing, and manages fallback logic.
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Full RAG Pipeline Implementation
- Simulation of document ingestion, indexing, embedding generation, retrieval processes, and development of a search interface with ranking metrics validation.
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Multi-Model Deployment Configuration
- Containerized deployment setup featuring Claude as the primary model and Ollama as a quantized fallback; monitoring configured via Grafana with defined alert thresholds.
Deliverables
- A shared Git repository containing validated code samples, API wrappers, and prompt test suites for agency review.
- Benchmark report detailing latency performance, token cost analysis, and code coverage metrics for decision-making support.
- Preconfigured Grafana dashboard for continuous monitoring of LLM interactions and system health.
- Comprehensive technical PDF documentation and a versioned prompt library to standardize implementation practices across government programs.
Troubleshooting Guidelines
Summary and Next Steps for Implementation
Requirements
- Demonstrated proficiency in at least one programming language, such as Scala, Python, or JavaScript.
- Competence with Git version control, REST API architecture, and continuous integration/continuous deployment (CI/CD) processes.
- Foundational comprehension of containerization via Docker and orchestration through Kubernetes.
- Commitment to leveraging artificial intelligence and large language model (LLM) technologies within enterprise software engineering contexts for government operations.
Target Audience
- Software Engineers and AI Specialists
- Technical Architects and Solution Designers
- DevOps Professionals deploying AI-driven pipelines
- Research and Development groups evaluating AI-enhanced development methodologies
35 Hours
Testimonials (2)
The course was very useful, and the trainer was clear, well-prepared, and engaging. I liked the fact that it was very practical with labs and real use cases. Overall, it was a valuable training experience.
Mattia Dettori - MFM INVESTMENT Ltd Italian branch
Course - LLM Engineering Bootcamp
1. The section on the constraints, tests, and guardrails was cool. 2. Francesco knows the topic very well, and he’s a nice teacher. 3. Overall, I really enjoyed the course, and I learnt a lot.
