You're in a Research Scientist interview at Google.Interviewer: We have a base LLM that's terrible at maths. How would you turn it into a maths & reasoning powerhouse?You: I'll get some problems labeled and fine-tune the model.Interview over.Here's what you missed: ...

Core Differences - DevOps focuses on software deployment and testing, with a straightforward feedback loop [1] - MLOps centers on model performance, addressing data drift and model decay over time [1] - LLMOps is foundation-model-centric, emphasizing optimization through prompt engineering, context/RAG setup, and fine-tuning [2][4] Monitoring & Evaluation - MLOps tracks data drift, model decay, and accuracy [2] - LLMOps monitors hallucination detection, bias and toxicity, token usage and cost, and human feedback loops [2][4] - LLMOps evaluation loop simultaneously feeds back into prompt engineering, context/RAG setup, and fine-tuning [3] Key Considerations for LLMOps - Prompt versioning and RAG pipelines are essential components in LLMOps [3] - Choosing the right ops layer should align with the system being built [3]

Core Differences - DevOps focuses on software deployment and code functionality [1] - MLOps centers on model performance degradation due to data drift and decay [1] - LLMOps emphasizes optimizing foundation models through prompt engineering, context/RAG setup, and fine-tuning [2][4] Monitoring Focus - MLOps tracks data drift, model decay, and accuracy [2] - LLMOps monitors hallucination detection, bias and toxicity, token usage and cost, and human feedback loops [2][4] LLMOps Unique Aspects - LLMOps evaluation loop impacts prompt engineering, context/RAG, and fine-tuning simultaneously [3] - Prompt versioning and RAG pipelines are essential components in LLMOps [3]

RT Avi Chawla (@_avichawla)I built my own ChatGPT from scratch, and you can too.Karpathy's nanochat is a single, clean, minimal, and hackable codebase to build a modern LLM.By setting this up, you'll learn how to:> train a tokenizer from the ground up> pre-training: master next-word prediction> mid-training: teach the model to hold conversations> sft: fine-tune on high-quality dialogue datasets> evaluate and log every step of the processI've done this on a LightningAI studio, and you can reproduce everythin ...

Technology & AI - The report highlights the possibility of building a personalized ChatGPT from scratch [1] - It references Karpathy's nanochat as a minimal codebase for building modern LLMs [1] - The setup process involves learning to train a tokenizer [2] - The setup process involves mastering next-word prediction through pre-training [2] Learning Objectives - The report focuses on learning how to train a tokenizer from the ground up [2] - The report focuses on pre-training to master next-word prediction [2]

Clone the @LightningAI studio to get started: https://t.co/1XntPP86Az ...

LLM Development & Training - The report introduces a method to build a modern LLM from scratch using Karpathy's nanochat, emphasizing its clean, minimal, and hackable codebase [1] - The process involves training a tokenizer, pre-training for next-word prediction, mid-training for conversational abilities, and SFT (fine-tuning) on high-quality dialogue datasets [1] - Evaluation and logging are integral to every step of the LLM development process [1] Implementation & Accessibility - The method can be reproduced with a single click on a LightningAI studio, requiring zero setup [1]

Core Problem with AI Agents - AI Agents relying on scraping websites and inferring meaning from HTML can lead to unpredictable behavior due to DOM shifts and pop-ups [1][2] - Prompt-engineering alone is insufficient for ensuring the reliable operation of AI Agents [2] - Agents require predictable contracts, which APIs provide through clear rules for requests, responses, and failure expression [2] Solution: Standardized API Access and Management - Standardizing API access, exposing APIs to Agents, and maintaining inspectability are crucial for debugging and monitoring [3] - Unified management, testing, and observability of raw API access are necessary to prevent chaos in the backend [3] - Teams building production-grade AI agents are converging on setting up infrastructure for API management [3] Postman's Solution for AI Agents - Postman offers a centralized hub for documenting, versioning, and accessing APIs [4] - Postman's MCP server exposes APIs directly to Agents, enabling them to call APIs without manual wiring [5] - Postman logs every request made by Agents with full history for debugging purposes [5] Key Takeaway - Agents understand interfaces, not intent; explicit interfaces lead to reliable behavior, while implicit interfaces lead to unpredictable behavior [5] - APIs, when built with the right infrastructure, provide the necessary context for AI [6]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/ocO8UZkuFaAvi Chawla (@_avichawla):Devs often treat Agent context as an access problem.The thinking goes like this:"If there's no API, just scrape the website. And if scraping breaks, let the Agent use the browser like a human."This sounds reasonable until you try to run it in production.To see why, https://t.co/mq7APfuOH6 ...

API Management - The guide focuses on solving the API problem [1] Industry Focus - Postman provides guidance related to APIs [1]