Agentic Solutions / Ensuring model consistency
Deliver safe, reliable and predictable LLM results
As companies increasingly leverage generative AI, maintaining model consistency becomes crucial. Ensuring that AI models deliver reliable and uniform results across different applications fortifies trust and enhances user experience.
How we can help
While it is often difficult to manage the inherent probabilistic nature of LLMs, our teams have deep experience applying a variety of foundational techniques to manage around this inconsistency and increase model performance in an effort to reduce latency, improve accuracy, and reduce costs.
Optimize the prompt
Our team employs advanced prompt engineering techniques, including few-shot learning, to effectively guide the model towards generating more accurate and consistent responses. By refining the input format and context, we help improve overall model performance while reducing latency and costs.
Ground the model
To mitigate the risk of "hallucinated" information, we implement retrieval-augmented generation (RAG) techniques that ground the model's output in relevant, factual data. Our approaches include simple retrieval, embedding formatting, metadata filtering, contextual retrieval, cross-encoder reranking, HyDE retrieval, chain of thought reasoning, auto-evaluation, and self-consistency.
Constrain model behaviour
We offer customization options to ensure your model behaves as expected, such as JSON mode for structured outputs, reproducible outputs using a seed for consistency, and fine-tuning to adapt the model's performance to specific domain knowledge or use cases. By tailoring the model's behaviour, we help you achieve reliable and predictable LLM results.
Companies we've helped
Three weeks to launch a scalable AI-powered marketplace solution with secure governance
In just three weeks, we built and deployed a secure human-in-the-loop matchmaking system for a service marketplace platform. Using generative AI and an open-source AI governance platform, the solution minimizes operational costs, accelerates lead response times, and scales without increasing headcount, with oversight, traceability, and control at its core.
Case Study
Babbly
An AI-enabled demo from concept to functional model in just 2 weeks
Babbly's founder needed a machine learning algorithm in a few weeks to close a pre-seed investment round.
Case Study
Blue J Legal
Simplifying the work of tax professionals with a mobile app
Blue J Legal successfully launched its AI-powered research tool and acquired its first paying customers during their partnership with Rangle.
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