Apr 29, 2024

Beyond Traditional Search — Enhancing Discovery with Contextual Insights

This blog breaks down the talk by Saurav Ganguly, SDE 2, GeekyAnts, at the Modern Web and Generative AI Development meetup, recently held at GeekyAnts.
Ahona Das
Ahona DasSenior Technical Content Writer

Through Saurav Ganguly’s presentation, the audience was treated to an insightful exploration of AI's potential to revolutionize search functionality. Saurav shared personal anecdotes and industry insights that shed light on the limitations of traditional keyword-based search and the promise of natural language processing (NLP) in refining search results.

Saurav Ganguly, SDE 2, GeekyAnts kicked off the presentation with a relatable scenario—New Year's resolutions. Everyone nods knowingly as he recounts the familiar struggle of attempting healthier choices, only to face the daunting task of navigating grocery store aisles armed with vague search terms and inadequate results online.

Understanding Search Mechanics

Understanding Search Mechanics

The presentation delved into the mechanics of search algorithms, emphasizing the limitations of keyword-based approaches. Saurav outlined the five essential steps involved in traditional search processes, from tokenization to indexing, illustrating how these steps can fall short when users don't use precise or expected terminology.

example of searching for "sugarless biscuits,

Using the example of searching for "sugarless biscuits," Saurav highlighted how keyword-based searches often yield irrelevant or misleading results. This flaw becomes more pronounced when users seek products based on dietary restrictions or nuanced preferences beyond conventional marketing terms.

Enter Natural Language Processing (NLP)

Natural Language Processing for user queries

Saurav introduced NLP as the solution to the shortcomings of keyword-based search. By leveraging NLP, search engines can interpret user queries more intelligently, considering context, synonyms, and user preferences to deliver more accurate and personalized results.

Commercial and Open Source Solutions

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The presentation covered a range of AI-powered search solutions available, from commercial platforms like Algolia and Salesforce Einstein Search to open-source frameworks like Elasticsearch and Meilisearch.

Saurav emphasized the importance of choosing a solution that aligns with the specific needs of the Fast-Moving Consumer Goods (FMCG) market.

DIY Challenges and Insights

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Drawing from personal experiences, Saurav shared the challenges of developing a custom NLP-based search solution. He highlighted the complexities of integrating NLP models into existing systems and underscored the importance of security considerations when deploying AI models directly within client applications.

Demonstrating a Custom NLP Prompt

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In a practical demonstration, Saurav showcased a custom NLP prompt designed to enhance search capabilities. The prompt illustrated how NLP can enrich search results by considering dietary preferences, allergens, and product attributes beyond conventional keywords.

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In closing, Saurav emphasized the transformative potential of AI in enhancing search functionalities. By adopting NLP-driven approaches, developers can create more intuitive, accurate, and personalized search experiences that cater to diverse user preferences and contexts.

Don’t miss out on the full presentation below ⬇️

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