Published on February 29, 2024, 6:16 am, a company founded by Lucky Gunasekara and Andy Hsieh, emphasizes the importance of delving deeper than the basics of RAG (retrieval augmented generation) when using generative AI for enterprise-level services. Their insight highlights the significance of understanding question context and assumptions to provide tailored answers efficiently.

Generative AI presents exciting prospects as it serves as an interface enabling users to interact with data through unique queries that yield customized responses. For instance, within a question-and-answer framework, generative AI tools act as query assistants, aiding customers in navigating vast product knowledge databases effortlessly.

Before harnessing generative AI to address inquiries regarding data, assessing the nature of the questions posed is crucial. Lucky Gunasekara, CEO of, offers this valuable advice to teams developing generative AI solutions today.

One notable project spearheaded by is Smart Answers, which utilizes generative AI to respond to questions related to articles on various websites such as, Computerworld, CSO, InfoWorld, and Network World. A similar Answers project was also developed for consumer technology websites including PCWorld, Macworld, and TechHive.

Gunasekara sheds light on the limitations of large language models (LLMs), indicating that these models may exhibit naivety in certain scenarios. He emphasizes the significance of evaluating questions before seeking relevant information to prevent biased outputs or misinformation from being presented to users.

The conventional setup for RAG applications typically directs LLMs to specific datasets without prior question evaluation which might lead to inaccuracies in responses. By incorporating an additional step to scrutinize assumptions inherent in questions before initiating a search for information, adopts a different approach that enhances the accuracy and relevancy of answers provided.

In addition to questioning assumptions within queries, Gunasekara suggests advancing beyond basic RAG pipelines particularly when transitioning from experimental stages towards production-ready solutions. Emphasizing the importance of considering contextual cues beyond just text semantics like recency and popularity can significantly enhance result outcomes.

Moreover, leveraging signals such as traffic patterns on a cooking website example provided by Gunasekara demonstrates how contextual clues play a pivotal role in refining search results based on user intent.

While emphasizing quality is crucial for LLMs’ efficiency, advocates for utilizing cost-effective solutions by fine-tuning their own models like Llama 2-based models instead of opting solely for high-priced commercial options. They highlight the growth in open-source LLMs as a promising alternative showing potential comparable performance levels even surpassing GPT-4 eventually.

In conclusion, Gunasekara’s team at showcases an innovative approach towards generative AI applications by prioritizing context cues alongside comprehensive question assessment methodologies ultimately leading to more accurate and tailored responses adapted for enterprise-grade services effectively.


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