Originally published on lavkesh.com
I've been experimenting with ChatGPT, which stands for Chat Generative Pre-Trained Transformer, a language model built by OpenAI that takes text as input and generates responses that sound human, powered by the GPT-4 engine, trained on massive amounts of text and refined through human feedback
The process of building ChatGPT involves two stages, pre-training and fine-tuning, where the model learns from a huge dataset of internet text, picking up grammar, facts, and how to reason about things, and then human reviewers craft examples to teach it to follow instructions properly and be more thoughtful about safety
ChatGPT understands context and generates coherent responses, making it useful for chat and customer support, and it can also summarize documents, translate between languages, write creative content, and even help with code, generating snippets, debugging, and explaining concepts
I first wired ChatGPT into a ticket triage bot for a mid‑size SaaS firm using the Azure OpenAI endpoint. The model sat behind a FastAPI layer, we capped each request at 2 k tokens to stay under the 4 k limit, and we added a Redis cache for repeat queries. In practice the latency hovered around 350 ms for typical user messages, but when the payload grew to near the limit we saw spikes up to 1.2 s, which forced us to fall back to a keyword‑based classifier for the tail. The cost per 1 M tokens was roughly $15 on the standard tier, so a busy support channel burned through $2 k a month, a number that made the business rethink the volume of free‑form queries.
One of the impressive things about ChatGPT is its ability to teach, explaining complex topics or helping with learning, and if you can describe it in text, GPT-4 can take a swing at it, from generating text to answering questions
However, ChatGPT struggles with long conversations, losing context and reflecting biases from its training data, and sometimes it makes up facts or sounds confident about something it doesn't actually know, which is why you should never trust it for critical information without verifying independently
To keep hallucinations in check we wrapped the model with a validation step that calls a vector store built on Pinecone. The prompt first asks the model to produce a concise answer, then we embed that answer and run a similarity search against a curated knowledge base. If the top hit falls below a 0.78 cosine threshold we flag the response for human review. In our deployment this filter caught about 42 % of the fabricated statements before they reached the end user, and it added roughly 120 ms to the overall round‑trip.
There's also the risk of misuse, from generating misinformation to creating deepfakes, which is a concern that needs to be addressed, and OpenAI is working to reduce bias and improve the model's performance
Looking ahead, the goal is to improve context handling, reduce hallucinations, and minimize bias, and ChatGPT has already changed how people work, shifting how we interact with text and code fundamentally, even if it's not magic and has real limitations
Monitoring turned out to be the hardest part. We instrumented the API gateway with Prometheus metrics for request count, error rate, and token consumption, and we fed those into Grafana dashboards that triggered PagerDuty alerts when the hallucination flag exceeded 5 % of traffic. The alerting window gave us just enough time to roll back a recent prompt template change that had unintentionally increased the model's tendency to over‑generalize. Without that feedback loop we would have let the issue linger for days.
I've seen firsthand how ChatGPT can help with tasks like writing and debugging, and while it's not a replacement for human judgment, it's a powerful tool that can augment our abilities and help us work more efficiently
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