Source proof for Nexthink fine-tunes a 7B Llama model on Amazon SageMaker to translate natural language into NQL queries for Nexthink Assist

…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…

…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…

…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…

…esponsible for translating natural language questions into precise NQL queries. Early versions of NL2NQL relied solely on prompt engineering. This approach involves carefully crafting instructions to guide the LLM’s behav…

…bmitted them to Claude 3.5 with a different role: not labeling but criticizing. The LLM acted as a judge, verifying that the LLM-generated label matched the query exactly; if there was doubt, the sample was discarded. Research shows that LLM-as-a-judge achieves over 80% agreement with human evalu…

…ced 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, Nexthin…

…ced 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, Nexthin…

…ced 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, Nexthin…

…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…

…turning natural language directly into NQL queries with high accuracy at scale. The team struggled with complex prompts, high costs from token usage, and slow response times when working with Nexthink’s full data model. These problems showed that using just a general-purpose LLM with prompts wouldn…

…e approach significantly reduced cognitive load and increased annotation speed. Nexthink used Amazon SageMaker Ground Truth to create a structured annotation workflow, enabling them to distribute tasks across their internal workforce, track progr…

…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…

…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…

…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…

…ken usage (-80%) and inference costs, as well as better handling of edge cases. The trade-off is increased complexity: fine-tuning requires substantial compute, high-quality datasets, and dedicated engineering effort. How Nexthink Evaluated and Fine-tuned Tools for Their AI Agents To develop and…

…Amazon SageMaker JumpStart , Customer Solutions , Generative AI Permalink Share AI agents powered by large language models (LLMs) are reshaping enterprise software by turning complex tasks into conversational experiences. Instead of requiring deep expertise, users can now interact with systems in nat…

…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…

…e approach significantly reduced cognitive load and increased annotation speed. Nexthink used Amazon SageMaker Ground Truth to create a structured annotation workflow, enabling them to distribute tasks across their internal workforce, track progr…

…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…

…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…

…esponsible for translating natural language questions into precise NQL queries. Early versions of NL2NQL relied solely on prompt engineering. This approach involves carefully crafting instructions to guide the LLM’s behav…

…ry . Instead of translating questions to NQL queries, they reversed the process—providing employees and interns with real anonymized NQL queries and asking them to write corresponding natural language questions. This inverse approach significantly reduced cognitive load and increased annot…

…with human evaluators , matching the 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 observed…

…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 da…

…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…