4/n) Next, I'm gonna try using the Contextual RAG technique by Anthropic as mentioned in one of their blogs.

What I love about the OpenAI SDK is how easily it generates structured output exactly the way I need. Here’s the prompt I used to generate relevant and a sneak peek at what the final metadata for a verse looks like:

However, in my case, the libraries missed a few entities, so I switched entirely to GPT-4o for metadata extraction. This lets me control quality exactly the way I want.

Link to the blog: www.elastic.co/search-labs/...

Here's how I usually write the prompt:
1) Start with a detailed, clear prompt - I try to provide as much information as I can and some context.
2) Then I ask ChatGPT for feedback and ask if there's anything missing.
3) Refine and test until it works well.

Most likely going to use an LLM to make this happen let’s see what kind of prompts work best. Open to any suggestions you might have!

When people visit this platform, they’ll likely seek advice on modern-day issues like “job stress” or “overthinking.” Since the verses don’t directly mention these, I’ll need to interpret them in a way that relates to today’s challenges.

Feel free to follow the project here:

github.com/AtharvaMaska...

3/n) We can calculate the mean and standard deviation of the trees planted by scaling the mean and standard deviation of the centuries scored accordingly.
This gives us a distribution having mean as 455.0 and standard deviation as 92.0

2/n) For every century Rohit scores, a non-profit plants 100 trees, plus 300 more on their own at the end of the campaign. How do we calculate the probability distribution of total trees planted from Rohit's score distribution? This is where density transformation shines!

1/n) Imagine a 3-match ODI series between India and South Africa. The probability distribution of Rohit Sharma's centuries in the series looks something like this:

6/n) If we can bring this system to remote corners where quality education is scarce, it could be a giant leap into the future. Next ambitious step? partnering with governments and NGOs to make education accessible, following the paths of @duolingoverde.bsky.social and @khanacademy.bsky.social.

5/n) This can be the foundations of a very sophisticated end-to-end autonomous educational system unlike anything we've seen before. This would further push the boundaries of what AI is capable of and its impact on making education more accessible and free for everyone

4/n) Now, the key here is asking the right questions so we can generate accurate student profiles. Using them we can mirror their thought processes, identify challenges, common mistakes and come up with creative solutions to help them overcome obstacles.

3/n) We can continuously adapt to students' preferences and learning styles by gathering real-time feedback. For those who learn best through visuals, we can create agents to build mind maps or diagrams. For story-driven learners, we can explain concepts through narratives.

2/n) Based on students' profiles, we can generate personalized examples and questions of varying difficulty levels on topics previously taught by the professor. This will increase cognitive engagement helping students absorb more knowledge and retain it longer.

1/n) Hyper-Personalized Student Profiles
Creating a detailed profile of each student, capturing their preferences, strengths, weaknesses, and interests through data on courses, extracurricular achievements, hobbies, and personal reflections from students about themselves.

Inspired by the incredible work @khanacademy.bsky.social and @duolingoverde.bsky.social are doing to make personalized education accessible, here are a few research directions I’d love to explore if given the chance.
You can check the paper out here:
arxiv.org/html/2410.04...

This is when I saw it as an opportunity to explore the pedagogical challenges and opportunities faced by students from diverse backgrounds and experiences, often with limited resources - challenges that AI has the potential to address.