Every week there’s a new AI model, framework, or buzzword. After shipping AI features in production for the past year, here’s what actually delivers value.

Structured Output is a Game Changer

The biggest shift isn’t smarter models — it’s reliable structured output. When GPT-4o and Claude can guarantee valid JSON matching your schema, AI goes from “cool demo” to “production feature.”

const result = await openai.chat.completions.create({
  model: "gpt-4o",
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "product_analysis",
      schema: {
        type: "object",
        properties: {
          sentiment: { type: "string", enum: ["positive", "negative", "neutral"] },
          topics: { type: "array", items: { type: "string" } },
          confidence: { type: "number" }
        },
        required: ["sentiment", "topics", "confidence"]
      }
    }
  },
  messages: [{ role: "user", content: reviewText }]
});

No more regex parsing. No more “sometimes it returns markdown.” Just clean, typed data.

RAG is Table Stakes Now

Retrieval-Augmented Generation went from research paper to must-have. If you’re building anything with AI and domain-specific knowledge, you need RAG.

The stack that works:

  • Azure AI Search or Pinecone for vector storage
  • Embedding model (text-embedding-3-large) for chunking
  • Hybrid search (vector + keyword) for better recall

The trick most people miss: chunk size matters more than model choice. Experiment with 256-512 token chunks before upgrading your model.

Agentic Workflows Are Real (But Fragile)

AI agents that can use tools, make decisions, and chain actions together — they work now. But reliability drops fast with complexity.

What works:

  • Single-tool agents (search, calculate, query database)
  • Two-step chains (classify → act)
  • Human-in-the-loop for critical decisions

What’s still risky:

  • Autonomous multi-step agents without guardrails
  • Agents making financial or legal decisions
  • Anything where “95% accuracy” means 5% disasters

Multimodal Input is Underrated

Vision APIs are incredibly powerful and underused. Real use cases I’ve shipped:

  • Invoice processing — photo → structured data
  • Quality inspection — product photo → defect detection
  • UI testing — screenshot → accessibility audit

Don’t sleep on this. The APIs are cheap and the accuracy is shockingly good.

What I’m Ignoring (For Now)

  • Video generation — cool but no production use case yet
  • Voice cloning — legal minefield
  • Autonomous agents — too unreliable for anything critical
  • Fine-tuning — RAG + good prompts beats fine-tuning 90% of the time

The Practical Takeaway

The AI features worth investing in today: structured output, RAG, single-purpose agents, and multimodal input. Everything else is either not ready or not necessary for most applications.

Build boring AI that works reliably. Save the bleeding edge for side projects.