New advancements in Generative AI

If you spent last year fine-tuning massive language models just to get decent JSON output, you already know how exhausting the state of the art was. Every project felt like you were wrestling a very smart, very expensive toddler.

Things have shifted. The narrative around generative AI has moved away from "look how big this model is" to "how do we make this thing actually reliable in production?" We aren't just prompting text boxes anymore. We're chaining agents, running smaller models locally, and dealing with structured outputs that don't randomly break at 3 AM because the LLM decided to get creative.

Here is what actually matters right now if you are building software with these tools.

1. Native Structured Outputs

For a long time, forcing an LLM to return valid JSON meant writing elaborate system prompts, crossing your fingers, and wrapping your parser in a brutal try/catch block. Even then, you'd eventually hit a day where the model felt like dropping a trailing comma or throwing a markdown code block inside your raw data.

Most major providers now support native structured outputs via JSON schema enforcement at the API level. The model literally cannot sample tokens that violate your schema.

Here is how you actually use this with the OpenAI Python SDK now, without praying to the probabilistic gods:

import os
from openai import OpenAI
from pydantic import BaseModel, Field

client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))


class CodeReview(BaseModel):
    summary: str = Field(description="One sentence summary of the code quality")
    bugs_found: list[str] = Field(
        description="List of potential bugs or security flaws"
    )
    rating: int = Field(description="Rating from 1 to 10")


completion = client.beta.chat.completions.parse(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "system",
            "content": "You are a senior code reviewer. Analyze the provided snippet.",
        },
        {"role": "user", "content": "print('Hello World') # TODO: add database"},
    ],
    response_format=CodeReview,
)

review = completion.choices[0].message.parsed
print(f"Rating: {review.rating}/10")
print(f"Bugs: {review.bugs_found}")

This saves you from writing custom validation loops. If the model's generation drifts, the API handles the rejection loop on their end.

2. Small Language Models (SLMs) Are Eating the World

Stop reaching for GPT-4 to classify support tickets. Seriously.

The biggest operational shift in the past six months isn't a new breakthrough in reasoning; it's that 8B and 7B parameter models (like Llama 3 or Mistral) got uncomfortably good. Running an 8-billion parameter model locally via Ollama or vLLM gives you sub-second latency, zero data privacy headaches, and a hosting bill that doesn't terrify your CFO.

I spent a weekend trying to migrate a simple text-categorization pipeline from a hosted API to a local Llama 3 instance. The setup looks something like this:

import json
import urllib.request

# Assuming you have Ollama running locally: ollama run llama3
url = "http://localhost:11434/api/generate"

payload = {
    "model": "llama3",
    "prompt": "Classify this support ticket as 'Billing', 'Tech', or 'General': 'My credit card was charged twice.'",
    "stream": False,
    "format": "json",
}

req = urllib.request.Request(
    url,
    data=json.dumps(payload).encode("utf-8"),
    headers={"Content-Type": "application/json"},
)

with urllib.request.urlopen(req) as response:
    result = json.loads(response.read().decode("utf-8"))
    print(result["response"])

The gotcha here? Context windows and prompt sensitivity. Smaller models are like brilliant interns. They are fast and cheap, but if you don't give them exact instructions with clear boundaries, they will wander off into the weeds much faster than a 400B parameter model. You have to prompt them tighter.

3. Tool Use and Function Calling Got Boring (In a Good Way)

Function calling used to feel like a fragile science experiment. You'd define a bunch of JSON schemas, parse the tool call response, execute your local database query, and feed the result back into the chat. Half the time, the model would hallucinate arguments that didn't exist in your schema.

Frameworks have matured, but more importantly, the base models learned how to use tools natively without needing giant orchestration frameworks holding their hands.

The biggest trap I fell into when learning this? Over-engineering agent loops. I tried building complex multi-agent setups using heavy orchestration libraries before I had a single stable tool call working. It was a debugging nightmare. Circular loops, infinite token burns, and zero visibility into why the agent decided to call the weather API three times in a row.

Start simple. Write a single function, bind it to the model, and handle the execution loop yourself in plain code before you introduce any agent frameworks.

# The mental model: handle tool execution explicitly
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_user_timezone",
            "description": "Get the timezone for a given user ID",
            "parameters": {
                "type": "object",
                "properties": {
                    "user_id": {"type": "string"},
                },
                "required": ["user_id"],
            },
        },
    }
]

# When the model responds with a tool_call, you run your standard Python function
# and send the result back in a follow-up message. No magic required.

When you write the execution loop yourself, you realize that "agents" are really just while loops with good marketing. Demystifying that changes how you architecture your apps.

Where to go from here

Don't try to master all of this at once. Pick a small, annoying utility script in your current codebase—something where you're currently regex-parsing messy text or writing brittle validation logic.

Spin up a local SLM using Ollama, or rewrite one of your API calls to use native Pydantic schema validation. See how much code you can delete.