Source proof for Nexthink fine-tunes LLMs on Amazon SageMaker for its Nexthink Assist AI agent

…AI agents must be accurate, specialized, and deeply aligned with domain logic. In this post, we share Nexthink ’s journey in fine-tuning LLMs on Amazon SageMaker to power their AI agents and make advanced capabilities accessible to every user. Democratizing DEX Insights Nexthink, a Swiss Software company is a pioneering l…

…sible to everyone, not just specialists. From Prompt Engineering to Fine-Tuning Nexthink Assist is an AI agent that acts as a DEX advisor, allowing users to ask questions in plain language and get answers instantly, w…

…o ask questions in plain language and get answers instantly, without expertise. This capability is a powered by a tool called NL2NQL, responsible for translating natural language questions into precise NQL queries. Early versions of NL2NQL relied solely on prompt engineering. This approach inv…

…tuning embeds domain knowledge directly into the model by updating its weights. This approach led to higher accuracy (+30%), more consistent outputs, lower token usage (-80%) and inference costs, as well as better handling of edge cases. The trade-off is increased complexity: fine-tuning requires substantial compute…

…cases. This required moving beyond manual annotation to an automated solution. The team implemented a new labeling pipeline where an LLM—specifically Anthropic Claude 3.5 Sonnet running on Amazon Bedrock —now generates the labels automatically. They evolved their initial labeling pipeline by adding an LLM component: Anthro…

…make advanced capabilities accessible to every user. Democratizing DEX Insights Nexthink, a Swiss Software company is a pioneering leader in digital employee experience (DEX). With a mission to empower IT teams and elevate workplace productivity, Nexthink…

…make advanced capabilities accessible to every user. Democratizing DEX Insights Nexthink, a Swiss Software company is a pioneering leader in digital employee experience (DEX). With a mission to empower IT teams and elevate workplace productivity, Nexthink…

…make advanced capabilities accessible to every user. Democratizing DEX Insights Nexthink, a Swiss Software company is a pioneering leader in digital employee experience (DEX). With a mission to empower IT teams and elevate workplace productivity, Nexthink…

…mation, Infinity enables organizations to deliver an optimal digital workspace. Nexthink’s platform has been traditionally reserved for expert IT power users, but demand is growing across the organization: other IT teams, HR and even employees want the same level of insights. Nexthink offers advanced analytical capabilities including a specialized query…

…k Query Language (NQL) to retrieve information for analyzing complex IT issues. While powerful, these capabilities create a barrier for non-technical users. The emergence of LLMs and AI agents creates the ideal opportunity to finally de…

…ved accuracy by providing relevant documentation and examples during inference. Using Amazon OpenSearch vector database, the system leverages the most relevant examples and documentation during the retrieval phase and injects them into the prompt to guide the model’s output during generation.…

…k leveraged SageMaker’s capabilities to build their AI-powered query assistant. The team used SageMaker’s distributed training capabilities to fine-tune a 7 billion parameter LLama LLM, incorporating advanced techniques like Low Rank Adaptation (LoRA). SageMaker’s managed infrastructure allowed Nexthink to focus on model quality r…

…e agreement rate between humans themselves. To safeguard against quality drift, Nexthink still had human experts periodically review samples of LLM-labeled queries. While this approach allowed to gain in speed and consistency, Nexthink observe…

…decision paid off significantly: the results after fine-tuning were impressive: a 30% improvement in query accuracy, 80% reduction in token usage, and comprehensive support across their entire data model. Best Practices for Enterprise Fine-Tuning Nexthink journey surfaced several be…

…weights. This approach led to higher accuracy (+30%), more consistent outputs, lower token usage (-80%) and inference costs, as well as better handling of edge cases. The trade-off is increased complexit…

…antly, without expertise. This capability is a powered by a tool called NL2NQL, responsible for translating natural language questions into precise NQL queries. Early versions of NL2NQL relied solely on prompt engineering. This approach in…

…olution. The team implemented a new labeling pipeline where an LLM—specifically Anthropic Claude 3.5 Sonnet running on Amazon Bedrock —now generates the labels automatically. They evolved their initial labeling pi…

…od their data structures without needing extensive explanations in each prompt. Amazon SageMaker AI provided the infrastructure and tools needed to fine-tune LLMs efficiently. Nexthink leveraged SageMaker’s capabilities to build their AI-powered query ass…

…decision paid off significantly: the results after fine-tuning were impressive: a 30% improvement in query accuracy, 80% reduction in token usage, and comprehensive support across their entire data model. Best Practices for Enterprise Fine-Tuning Nexthink journey surfaced several be…

…antly, without expertise. This capability is a powered by a tool called NL2NQL, responsible for translating natural language questions into precise NQL queries. Early versions of NL2NQL relied solely on prompt engineering. This approach in…