Small Language Models vs Large Language Models for Enterprise AI
Compare small and large language models for enterprise tasks: domain quality, deployment, privacy, latency and total cost, with a practical decision flow.
Harshad PatelENGINEERING BLOG
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19 articles in Generative AI & LLMs
Harshad Patel
LLM Inference Cost Optimization: Model Routing, Batching and Semantic Caching
Optimize inference with evaluated model routing, suitable batching and safe caching. Includes a token-cost calculation, explicit assumptions and benchmark worksheet.
Mansukh Marakana
How to Test LLM Applications: Regression Suites, Golden Datasets and Red Teaming
Build LLM regression suites with golden datasets, reusable cases, failure categories and controlled red teaming. Includes a practical release scorecard.
Vimisha K
LLMOps Architecture: CI/CD, Model Evaluation and Deployment for Enterprise LLM Apps
Design an enterprise LLMOps pipeline with versioned prompts, evaluation gates, staged deployment, monitoring and rollback ownership. Includes a release manifest.
Paresh Purohit
MLOps vs. Traditional DevOps: Taking AI Models to Production
Building a machine learning model is only half the job. MLOps extends DevOps practices to data and models so AI keeps performing reliably…
Harshad Patel
Generative AI for Business: Practical Use Cases Beyond Chatbots
Generative AI can do far more than answer chat questions. From document intelligence to code generation, these are the use cases delivering real…
Mansukh Marakana
Choosing Between OpenAI, Claude and Open-Source LLMs for Your Product
There is no single best large language model. A practical framework for choosing between OpenAI, Claude and open-source LLMs by task, data rules,…
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