Writing good answers in UPSC Mains is a skill.

Like all skills this can be learned.

Here's what makes for a good Mains answer

= Thread =

Disclaimer

Before we start, these are borrowed ideas.

Borrowed from:
• Gaurav Agarwal @crazyphoton (Rank 1, CSE2013)
• Anudeep Durishetty @anudeepd7 (Rank 1, CSE2017)
• Pratyush Pandey (Rank 21, CSE2019)

Even Pratyush and Anudeep mention @crazyphoton as the OG.
Structure

Your answer needs to have structure.

This makes it easier to read, and ensures that you have covered all parts of the question asked.

Let's look at what makes for good structure.
Introduction

Keep it simple.
Choose one word from the question and define it.
Quote facts or relevant articles if possible.

This one line about the topic let's the examiner know you've understood the question.

Example:
Body

Break it into 3 parts:
• Context - what has happened so far
• Main argument
• Counter argument

Give counter arguments (one line works) even when not asked. This adds another angle to your answer.

UPSC is looking for breadth, not depth.
Conclusion

Don't use vague phrases like:

"Thus parliamentary sovereignty must be safeguarded."

"Hence a balance must be stuck between the three powers."

etc
etc
Conclusion II

When possible, quote:
• Committees/Commissions recos
• SC judgements
• Relevant current events

This adds that extra kick to your answers.
Gets you that extra half mark.

P.S. This is what you would need to do as an IAS/IPS.
Why is this structure necessary?

The examiner checking your scripts is not incentivised to look for information in your answer.

Make the information visible.

Make their job easier.

Structure helps them check off, easily, whether you've covered all angles.
Thinking in Frameworks

There will always be questions in Mains that you don't know so well.

What do you do in such cases?
Apply frameworks, like PESTEL or MEDICS (in IR).

Using frameworks will allow you to write even when you don't have specific information.
Content

You would need:
• Notes for quick revision
• A fact sheet

In your content:
• Understand the TRENDS
• Note relevant solutions/way forward
• Supplement with CA

Finally, good language helps, but not necessary.

Content is King!
Presentation

Good presentation is crucial.

Why you ask?
A neat and clean answer makes it easier for the examiner to give you marks.

How do you improve presentation?
• Structure
• Diagrams, maps & flowcharts
• Minimise errors
• Underline & highlight
Feedback

You’ve written the best answer
ACCORDING TO YOU

Feedback helps you work on your blind spots.

Feedback also helps you inculcate best practices for acing CSE Mains.

Take some feedback.
You need to go from

Unconscious incompetence
to conscious incompetence
to conscious competence
to unconscious competence.

Practice will take you there.

Also, I'm forming a Mains Focus Group.
Interested? Fill this form: https://t.co/V6CXURidjC
For more tweets like this, follow @abhiwhy

And if you liked this thread, please RT the first tweet.
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https://t.co/j7v5yfpsQC

More from All

How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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