Buzz-Free Programming with LLMs

TRAINING DESCRIPTION

This training provides a pragmatic, no-nonsense approach to working with Large Language Models (LLMs). Instead of relying on hype, it emphasizes foundational techniques—effective prompts, solid context management, and time-tested programming principles. Participants will gain insights into building reliable and maintainable LLM-based applications using SpringAI or LangChain, without unnecessary complexity.

Additionally, the course covers evaluating LLM outputs, implementing observability practices, and managing deployment and scaling concerns. By the end of the training, participants will be equipped to deliver robust, efficient, and production-ready LLM solutions.

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BASIC PROGRAM

  • Module 1: Core Foundations of LLM Integration
  • Module 2: Prompt Engineering Essentials
  • Module 3: Context Management and State Handling
  • Module 4: Leveraging Classical Programming Approaches
  • Module 5: Avoiding Overhead: Minimizing Agents and Abstractions
  • Module 6: Real-World Implementations with SpringAI and LangChain
  • Module 7: Evaluation and Testing of LLM-Based Systems
  • Module 8: Observability, Deployment, and Scaling

DETAILED PROGRAM

Module 1: Core Foundations of LLM Integration

  • Understanding LLM capabilities and limitations
  • Embracing simplicity over hype
  • Recognizing prompts and context as key drivers of model behavior
  • Introduction to SpringAI and LangChain for direct integration

Module 2: Prompt Engineering Essentials

  • Writing clear, targeted prompts for predictable results
  • Iterative prompt refinement techniques
  • Multi-step prompts for complex scenarios
  • Practical examples (summarization, Q&A, classification)

Module 3: Context Management and State Handling

  • Providing relevant context to guide the LLM effectively
  • Managing token constraints and preserving information across requests
  • Integrating reference documents and external data sources
  • Ensuring consistency and coherence in multi-turn interactions

Module 4: Leveraging Classical Programming Approaches

  • Integrating LLMs into standard application architectures
  • Handling input/output validation, error cases, and logic branching
  • Structuring code for maintainability and testability
  • Applying familiar coding principles (e.g., SOLID) to LLM-based solutions

Module 5: Avoiding Overhead: Minimizing Agents and Abstractions

  • Understanding the purpose and drawbacks of agents, tools, and layers in LangChain
  • Knowing when higher-level abstractions add real value—and when they don’t
  • Achieving similar results with careful prompt design and basic programming
  • Reducing complexity while maintaining flexibility and scalability

Module 6: Real-World Implementations with SpringAI and LangChain

  • Setting up SpringAI or LangChain in practical projects
  • Developing end-to-end solutions (e.g., chatbots, data-driven assistants)
  • Ensuring performance, reliability, and maintainability in production
  • Deployment considerations for stable, long-term solutions

Module 7: Evaluation and Testing of LLM-Based Systems

  • Defining quality metrics for LLM outputs
  • Techniques for automated and manual testing of model responses
  • Using test scenarios, golden data sets, and regression tests
  • Incorporating feedback loops to continuously improve LLM performance

Module 8: Observability, Deployment, and Scaling

  • Implementing logging, monitoring, and tracing to gain insights into LLM performance
  • Strategies for scaling LLM-backed applications without overcomplicating the architecture
  • Balancing resource usage and latency targets
  • Best practices for deploying updates, rolling back changes, and ensuring stable operation

KEY TAKEAWAYS

  • Master essential skills for working with LLMs without falling into hype-driven complexity.
  • Craft effective prompts and manage context to achieve accurate, consistent results.
  • Test, observe, deploy, and scale LLM-based applications using robust programming principles.
  • Know when to rely on direct techniques vs. introducing agents, tools, or abstractions.
  • Deliver production-ready, maintainable, and scalable LLM solutions with confidence.