Hi, I am Rama Ramakrishnan
Welcome! I am a Professor of the Practice in AI/ML at MIT Sloan. These articles are my ongoing effort to demystify AI and make it practical. I hope you find them useful (feedback is welcome: either email me at
or send me anonymous feedback via this link).
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Browse by Topic: AI Demystified | Practical AI | Practical ML/Data Science | Practical Python | Random
Recent
How Google built an LLM-based personal health coaching system. (August 2025)
AI vendors struggling with enterprise adoption are hiring "Forward Deployed Engineers". What exactly is this role? (August 2025)
To make ChatGPT into a personalized tutor, you just need a two-page prompt. (August 2025)
Interested in AI for healthcare? This is worth a look. (July 2025)
AI systems win gold medals at the 2025 International Math Olympiad. (July 2025)
With GenAI, previously un-economical tasks may become viable. (July 2025)
How LLMs got better at "reasoning": A non-technical explainer. (July 2025)
AI Demystified
Created with love for the curious. A technical background is NOT necessary.Extract just what you need from long LLM responses (August 2024)
Using LLMs in business: A few guidelines (May 2024)
How to use LLMs as "helpers" to build and customize other LLMs (January 2024)
Get better answers from ChatGPT by using custom instructions (December 2023)
How ChatGPT works (a 20-minute explainer video) (September 2023)
How ChatGPT can answer complex questions using external tools (June 2023)
Practical AI
How Google built an LLM-based personal health coaching system. (August 2025)
AI vendors struggling with enterprise adoption are hiring "Forward Deployed Engineers". What exactly is this role? (August 2025)
To make ChatGPT into a personalized tutor, you just need a two-page prompt. (August 2025)
Interested in AI for healthcare? This is worth a look. (July 2025)
AI systems win gold medals at the 2025 International Math Olympiad. (July 2025)
With GenAI, previously un-economical tasks may become viable. (July 2025)
No time to learn prompt engineering? Do this instead. (June 2025)
Which AI tool should you use? (June 2025)
LLMs keep crushing tough benchmarks. Does this mean we are closer to AGI? (June 2025)
Are your AI apps "infuriatingly useless horseless carriages"? (June 2025)
If you are using LLMs for "serious" applications, you must check-and-fix hallucinations (May 2025)
If we can understand exactly what an AI model "knows" and how it "thinks", we can make it safer AND more reliable (April 2025)
Is learning from experience data (rather than human-generated data) the key to superhuman AI? (April 2025)
Solving RAG's "lost context" problem (April 2025)
A Foundation Model for Tabular Data (March 2025)
Using LLMs to write code - Advice from Simon Willison (March 2025)
Getting payback from Generative AI (Sloan Management Review webinar) (March 2025)
Using GenAI to create no-code prototypes (December 2024)
Generative AI as a New Platform for Applications Development (September 2024)
Working with LLMs? Keep these things in mind. (September 2024)
Identifying AI opportunities in business - don't overlook the things NOT being done (April 2024)
The AI/ML/GenAI landscape (short videos) (March 2024)
Practical ML/Data Science
How big should your sample size be? A handy little formula that every data scientist should know (December 2022)
6 steps for leading successful data science teams (August 2021)
How to Learn Optimal Policies From Data: Part 1, Part 2, Part 3, Part 4 (June-August 2021)
Lessons from a Deep Learning Master (July 2020)
An Alternative to the Correlation Coefficient That Works For Numeric and Categorical Variables (June 2020)
Data Scientists, Ask Yourself Often: So What? (June 2020)
How to Use Causal Inference In Day-to-Day Analytical Work (Part 2 of 2) (June 2020)
How to Use Causal Inference In Day-to-Day Analytical Work (Part 1 of 2) (October 2019)
Create a Common-Sense Baseline First (January 2018)
I Have Data. I Need Insights. Where Do I Start? (July 2017)
Handy Command-Line One-liners for Starting Data Scientists (June 2017)
One More Reason to Prefer Simple Models (April 2016)
Three Ways to Analytic Impact (July 2011)
How Good Management Can Produce Bad Data (Nov 2010)
Practical Python
Useful Pythonic things that I will probably forget and have to re-learn if I don't write them down somewhere :-)(each entry is written as a Jupyter/Google Colab notebook so that you can run the code and play with it directly)
How to add color to your boring text output (and build a
while you are at it!)
Find long Python statements annoying? Here's how to break them up
Be careful when you pass lists, dicts or sets into a function
Easily process arguments for your command-line script with argparse
To store related variables (e.g., hyperparameter configs), don't use dictionaries
Random
Curious about how drugs are discovered? Here's a book recommendation. (June 2025)
Don't fall into the "fast food reading" trap (April 2025)
If you like math/science biographies, you will LOVE this biography of David Hilbert (April 2025)
Nabeel Qureshi's Life Principles - The ones that resonated with me (December 2024)
How to read without slipping into “check the box” mode (May 2022)
You can be - A book for little girls (by Anu Chitrapu) (July 2021)
A Peek into the Incomparable Mind of Isaac Asimov (May 2020)
Just for fun: A Sudoku Solver in Python (May 2019)
Building startups in an exit-friendly way (Jan 2017)
Can Animals Perceive Human Relationships? (Jan 2016)
What an Educated Person Should Know – Steven Pinker (Jan 2016)
The Taste Of This Meal Is Affected By The Room We Sit In (Jan 2016)
The User is Never Wrong (Dec 2015)
Three Supernovae Every Night! (Dec 2015)
Tiponomics: Analytics for Waiters (July 2010)
Farewell to Martin Gardner (May 2010)
The Ascent of Ranking Algorithms (April 2010)
Smarter Cruise Control With Analytics (March 2010)
Applying Behavioral Economics To Retail (March 2010)
Factoids, Stories and Insights (March 2010)
On Cuts and Clutters (my Ph.D. thesis from a long time ago :-))