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Borrowing Idea of Lisp into LLM systems
In our earlier blog, we gave a gist using DSL for the LLM problems that requires agent feedback and deciding the execution phase accordingly. In a way this is a followup for our earlier blog on function calling.
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Busting common Myths in developing bespoke LLMs
The advent of the LLM revolution marks a transformative era, empowering Machine Learning models to elevate businesses to heights previously unattainable.
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Function Calling?Nah! We got something better!
Explore an alternative method that can enhance the efficiency and ensures the highest quality in your LLM based solutions.
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The Gen AI Dilemma: Balancing Probabilities and Precision in Enterprise Integration
In today’s tech landscape, nearly every product comes with a “.ai” tag, proudly showcasing the integration of Gen AI features.
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GraphQL: The Superior Choice for LLM-Based Applications
Through an extensive empirical study, we explored the most effective ways to deliver Gen AI solutions, powered by LLMs, and identified GraphQL as the superior choice over traditional Traditional REST, Holistic OpenAPI, and Elasticsearch for structured queries.
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Semantic Search: Transforming the Search Experience for the Modern Users
In this context, we will explore evolution of searches, starting with traditional keyword-based searches and then delving into a more advanced approaches.
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Use LLMs only if you have to!
When solving traditional problems like prediction and classification, the answer to the question, “When do you need an ML model?” was clear: “We need ML models for problems that cannot be solved with engineering and heuristics.”.
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