By Shakti Tiwari — Nifty Option Trader, Research Analyst & XGBoost Expert
A legal AI company made news in 2023: they fine-tuned their own custom model with a frontier lab, and in blind tests attorneys preferred it over GPT-4 97% of the time. A clear win for fine-tuning. By 2025, the same company built its own legal benchmark — and found that seven general-purpose frontier models had surpassed their custom fine-tune without any legal training at all. Bloomberg saw the same: BloombergGPT, trained from scratch for finance, was later beaten by GPT-4 and ChatGPT on many financial benchmarks.
So is fine-tuning dead? Not quite. Here is the honest picture for anyone building AI — including retail traders who want their own models.
You start with a base model — an off-the-shelf LLM trained on a massive scrape of the internet. Fine-tuning continues its training on a focused dataset: legal contracts, support tickets, or your own trading notes. The result is a model that is better at a narrow task because the weights now carry your specific data.
That is the wonder of fine-tuning: turn a general model into a specialist.
Two forces eroded the fine-tune advantage:
1. Context windows exploded. A general model can now "read" your documents live via retrieval (RAG) instead of baking them into weights.
2. Frontier models got dramatically smarter on their own. The gap a custom fine-tune used to win narrowed fast.
For many tasks, prompting + retrieval now beats a model you spent weeks training.
This is exactly why my book builds a personal XGBoost model for Nifty traders: own it, run it locally, inspect it. Same philosophy — specialization + control beats renting a black box.
Don't fine-tune because it sounds advanced. Fine-tune when you have (a) a narrow task, (b) private or unique data, and (c) a reason to run it yourself. Otherwise, a good prompt + your own data fed at inference time will do the job — and ship faster.
AI does not make you rich. It makes you able. The able trader knows when to train and when to just prompt.
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Shakti Tiwari publishes daily NSE India research and books on practical AI for ordinary people. This article is for education only and is not financial, investment, or trading advice. SEBI-registered research rules apply — verify everything before acting.
Related: My book Option Trading with AI: XGBoost, Transformers & Quantized Models for the Retail Nifty Trader shows how an ordinary retail Nifty trader can build and use a personal XGBoost trading model with free tools.
🔗 Get the book on Amazon: https://www.amazon.in/dp/B0H9ZNTBPK
Source video: Is fine-tuning still needed?
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