Building AI applications in 2024 looks nothing like it did in 2022. The tools have evolved dramatically, and they’re still changing fast.

Here’s a developer-focused overview of the current AI development landscape.

The Stack Has Shifted

Traditional ML development required deep expertise in model training, data pipelines, and infrastructure. The new stack is different:

Foundation models as building blocks: Instead of training models from scratch, developers increasingly use pre-trained models (GPT-4, Claude, Llama) as starting points.

API-first development: Most AI capabilities are now accessible via API rather than requiring local deployment.

Prompt engineering as skill: Getting good outputs from AI models is now a development discipline.

Orchestration over training: Building applications that combine and coordinate AI capabilities rather than creating new AI.

Key Tools and Frameworks

LangChain: The dominant framework for building LLM applications. Handles prompt management, chains, memory, and integrations. Essential knowledge for AI developers.

LlamaIndex: Specialized for building applications that query your own data. Excellent for RAG (Retrieval Augmented Generation) implementations.

Semantic Kernel: Microsoft’s AI orchestration framework. Strong if you’re in the Microsoft ecosystem.

Haystack: Open-source framework for building search and QA systems. Good alternative to LangChain for certain use cases.

Vector Databases: Pinecone, Weaviate, Chroma, Milvus—essential for storing and searching embeddings. Pick based on scale, hosting preference, and features.

Development Patterns

Common patterns in modern AI development:

RAG (Retrieval Augmented Generation): Query your own documents/data to provide context for AI responses. The most common pattern for enterprise AI applications.

Agents: AI that can take actions, use tools, and complete multi-step tasks. More complex but increasingly important.

Fine-tuning: Customizing foundation models for specific tasks. Less common than it sounds—often RAG is sufficient and cheaper.

Prompt chains: Breaking complex tasks into sequences of simpler prompts. Essential for reliability.

What to Learn

If you’re a developer entering AI application development:

Start here:

• Python (if you don’t already know it)
• Basic understanding of how LLMs work (not math-deep, but conceptual)
• LangChain or similar framework
• Vector database basics

Then expand:

• Prompt engineering best practices
• RAG implementation patterns
• Agent development
• Evaluation and testing for AI systems

Advanced topics:

• Fine-tuning when and how
• Multi-modal AI (text + images + audio)
• AI system architecture at scale
• Security and safety considerations

The Evaluation Problem

Building AI applications is easier than evaluating them. This is a real challenge:

Non-deterministic outputs: Same input can produce different outputs. Traditional testing doesn’t quite work.

Quality is subjective: What makes a “good” AI response varies by context.

Edge cases are infinite: AI fails in unexpected ways you can’t enumerate in advance.

Evaluation tools are immature: This space is developing but less mature than development tools.

Expect to invest significant effort in evaluation frameworks, human review processes, and monitoring.

Production Considerations

Getting AI applications to production involves:

Latency management: LLM calls are slow. Caching, streaming, and async patterns matter.

Cost optimization: API calls cost money. Optimize prompt length, cache strategically, consider when to call AI vs. simpler methods.

Reliability: External API dependencies fail. Build in fallbacks and graceful degradation.

Monitoring: Track inputs, outputs, latency, errors, and costs. Essential for production AI.

Security: User inputs to AI models create new attack vectors. Prompt injection is real.

Build vs. Buy

For AI development, the build vs. buy decision is nuanced:

Buy/use existing:

• ChatGPT plugins for simple integrations
• Existing AI features in your tools (Copilot, Einstein, etc.)
• No-code AI builders for simple applications

Build with frameworks:

• Custom applications with specific requirements
• Integration with proprietary data
• Unique user experiences

Build with specialists:

• Complex applications requiring deep expertise
• Enterprise-grade reliability requirements
• Novel applications without existing patterns

Many organizations benefit from starting with existing tools, then engaging specialists for custom development as needs become clearer.

The Human Element

Despite all the tools, AI development still requires significant human judgment:

• Defining what “good” looks like for your application
• Deciding when AI is appropriate vs. traditional approaches
• Handling edge cases and failures gracefully
• Maintaining systems as AI capabilities evolve

The tools are getting better, but they don’t eliminate the need for thoughtful development.

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Exploring the evolving landscape of AI development tools and practices.