Intelligence with GenAI

When learners can see 'Intelligence with GenAI'. It is very heartening to see solutions built during the session :)

Some feedback after the 12-hour GenAI session:

• We have a lot of data; we can use LLM to add intelligence. A lot of IoT sensor data is present, but only fixed reports are available.
• I have only used #ChatGPT; now I see a ton of tools to explore.
• Infra intelligence - LLM is used to understand the complete ecosystem of servers, leverage logs + intelligence to find server owners, and alert on reallocation / upgrades.
• Automate Data Analyst work to analyze keywords, user query patterns. This saves 65% of my efforts.

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More Smarter Agents + LLMs = Superior Intelligence

 

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Latency is a never ending learning

• Latency with cache
• Latency with semantic cache
• Latency with Indexes
• Latency with Graph Queries
• Latency with Optimal Value for Top K

Benchmark against domain dataset

Good Data = Good Strategy = Quality Experiments

Happy Low Latency!!!

How to avoid this scenario - Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025

What is required to Turn Data Into AI Products ? - My perspectives

• Experimentation on Different levels of data / Summary / Key-Values / Multimodal
• Data curation / Setting up Relationships
• Adding domain knowledge

The main reasons cited are:

• Poor data quality
• Inadequate risk controls
• Escalating costs
• Unclear business value

So, the lesson is, my perspectives are:

• The model is not lift-and-shift — customize it for your needs. A demo that works may not be the solution you need.
• Build your data and benchmark with your data. Do not rely on benchmarks that do not reflect your data.
• Have an LLM cybersecurity, data governance, and guardrails in place. Do not trust the LLM until your first 100 users are happy with it.
• Escalating costs—first, get the accuracy right, then reduce costs. You cannot achieve everything at once.
• Unclear business value — Do not force-fit LLM use case to get a promotion. Only opt for it if it genuinely adds value.
• If someone promises a working solution in 1 month remember it can be selling a prototype, not a production-grade solution

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Working as AI Advisor

• Close proximity to decision-makers
• Tech will be commodity but Domain + Tech is true knowledge
• Loyalty works in long term, talent gets visibility in short term
• Take time and provide accuracte inputs, Optimism + Clear perspectives matter
• Building relationships, Experimenting on meaningful projects, Novelty to build solutions / integrate past experience + latest tech to match quality matters
• Balancing business vs tech roadmap, Being able to spot features that differentiate the rest
• In the end everything may fail but there is a satisfaction in the Journey

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Painful Moments - Potential People vs. Wrong Mindset

While evaluating answers: Some candidates document well, attempt, and submit answers but miss the basics. This reflects both intent and missed guidance in learning. High potential is evident, but basics are either overlooked or dot-connecting skills are lacking.

While teaching: Some PhD/lateral folks tend to generalize everything or focus on proving theories break. Learning is not about proving your knowledge but about gaining a balanced perspective. One class is not sufficient to judge anything. Observing these types of learners makes me feel sad as they are so short-sighted.

Education is not mindset; experience does not mean competency!!!

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Memorization vs Generalization

Memorization vs Generalization

When you develop #GenAI apps, After a certain stage, When things work fine, The immediate next question is

• Model is memorizing or Learning patterns
• Test with variations / Analyze on patterns of responses

I don't want my life to be memorization - Company1 - Company2 ..., Exploring out of comfort zones provides diverse perspectives.

Earlier I had time to regret, Now I don't have time to think about anything. A long day of managing and solving different problems and different lenses of execution. Sometimes some experiences don't fill your pocket but fill your soul. In the end, I want to smile at death, I have tried all my wishlists.

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GenAI - LLM - Startup Learning War

 

Latency Experiments - Link

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The Uniqueness of Customer Data and the Importance of Automated Cleanup

In the digital age, managing customer data can be a daunting task. Here are some points to consider:

• Each customer's data will be unique.
• Each customer's data will encounter parsing issues.
• Each customer's data will disrupt pipelines.

No data is pristine; changes should not be spontaneous. When dealing with data, especially on a large scale, it's imperative to have a robust process in place. Having a process to automate cleanup is essential to scaling your solution.

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