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Title: TechScribr

Publisher:  sayan.biswas
Message frequency:  0.14 / day

Message History

Packing Intelligence into Fewer Bits: Non-Linear Quantization in LLMs
A 70-billion-parameter LLM stored in 16-bit floats needs roughly 140 GB of memory, more than most GPUs can hold. Quantization shrinks the model by replacing those 16-bit floats with much smaller integers (4-bit, for example), cutting memory by $4\times$. The simplest approach is linear quantization, which spaces the quantized values evenly across the weight range, like the marki...

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Decoding RAG Evaluation: When Your Pipeline Fails, Who is to Blame?
Retrieval-Augmented Generation (RAG) has rapidly become the enterprise standard for bridging the gap between static Large Language Models (LLMs) and dynamic, proprietary data. By fetching relevant documents and injecting them into the LLM’s prompt, RAG promises accurate, hallucination-free answers. But what happens when the system generates a bad answer? If you simply look at th...

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Epilogue: The Grand Unifying Theory of A/B Testing (Enter the GLM)
Welcome to the epilogue of our six-part series on experimentation and A/B testing! Over the past few months, we’ve covered a massive amount of ground. We started with the foundational statistics of p-values and z-tests, navigated the tricky waters of Sample Ratio Mismatch (SRM), ventured into the complex realm of causal inference (DiD, PSM, IV), and finally arrived at the cuttin...

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Beyond Traditional A/B Testing: Multi-Armed and Contextual Bandits
Welcome to the final installment of our A/B Testing series! Over the past several posts, we’ve covered the entire statistical foundation of experimentation - from p-values, confidence intervals, and z/t-tests to ANOVA, Chi-Square tests, sample size calculations, and even advanced causal inference methods like DiD, PSM, and IV. If you’ve followed along, you are now equipped to de...

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Beyond A/B Testing: Causal Inference in the Wild (DiD, PSM, and IV)
If you’ve been following my series on statistical testing, you’re already comfortable with 2-sample t-tests, ANOVA, and Chi-Square tests. Those tools are fantastic for randomized, perfectly controlled A/B tests. But what happens when you can’t perfectly randomize? What happens when the real world gets messy? In this article, we are stepping into the realm of Causal Inference. We...

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