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❯ DineshKumar's Blog
You built a brilliant GenAI feature and now finance is asking why your infrastructure costs tripled
You added an “AI co-pilot” to your SaaS product. Customers loved it. Usage exploded. Token costs scaled linearly with usage. Your margins inverted.
Your LLM is a brilliant liar and your users can tell
You deployed RAG. Documents went into Pinecone. Queries hit the vector store, retrieved relevant chunks, fed them to the model. It worked beautifully for the first month.
You’re bleeding $200/month on API calls and your research notes are scattered across five apps
You ran a quick query to Claude at 3am for context on a paper, then checked ChatGPT because you needed a different model’s take, then tried Perplexity for web search. Three separate tabs, three separate contexts, no memory of what you asked yesterday.
You spent a week tuning a BERT model and it still can’t generate coherent text
Your team trained a bidirectional encoder on your company’s knowledge base. It’s great at retrieval and classification but useless for generation — because it wasn’t designed for it. Meanwhile, a 7B decoder-only model you’ve never seen before is generating better answers than your 50M-parameter expert model.
You just got paged at 2am because the model is hallucinating
Your ML model trained on 5 years of labeled customer data is confidently generating support tickets that never happened. Your team’s trained on classifier debugging — check the confusion matrix, retrain on edge cases, deploy. But that playbook doesn’t work here. The model didn’t misclassify; it invented facts.
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