# llm-evaluation Published articles for llm-evaluation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Bootstrap Confidence Intervals for LLM Evaluation DevFeed: [Bootstrap Confidence Intervals for LLM Evaluation]() Original publisher: [Read original article]() Author: James Fiedler Published: 2026-07-08T14:30:02Z Content type: article Language: en Sources: [Indeed]() Topics: [LLM evaluation / benchmarking](), [Large Language Model]() Tags: [analytics](), [batching](), [confidence-interval](), [data-science](), [developers](), [evaluation](), [llm](), [llm-evaluation](), [machine-learning](), [metric](), [performance](), [research](), [variance](), [vs]() ### AI overview This article explains how to construct confidence intervals for non-deterministic LLM evaluations using bootstrap resampling. It recommends resampling inputs while carrying along all model runs for each selected input, then using the 2.5th and 97.5th percentiles for a 95% interval. It also discusses the tradeoff between the number of inputs and repeated runs, recommending k = 3 or 5 when feasible. ### Source excerpt Introduction As Large Language Models (LLMs) move from research prototypes to production systems, the developers of these systems need rigorous performance evaluation. In particular, we need confidence intervals around estimates of system accuracy. However, LLMs introduce a challenge that is unusual for ML systems: they are (operationally) non-deterministic. Even with the temperature set to zero, [...] ## The model is the easy part: Building the LLM Platform at Whatnot DevFeed: [The model is the easy part: Building the LLM Platform at Whatnot]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-04-14T15:01:05Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [AI Platform](), [LLMs](), [AI Development](), [Tooling]() Tags: [ai-agent](), [ai-platform](), [building](), [llm](), [llm-evaluation](), [llms](), [platform](), [platform-engineering](), [production](), [prompt-engineering](), [quality](), [teams](), [trust](), [workflows]() ### AI overview This article explains that building an LLM platform involves much more than calling a model. At Whatnot, the platform is organized around reliability, velocity, and trust, supported by existing data, logging, analytics, integration, and internal tooling foundations. ### Source excerpt Stas Sajin, Faithful Alabi, Peiyun Zhang, Peicheng Yu | AI Platform Introduction A decade ago, one of the more useful ways to explain machine learning systems was with the diagram below: the ML model itself was a tiny box in the middle, surrounded by everything else you actually had to build to make it work in production. Figure 1: Complexity surfaced by ML systems. Source. The same thing is happening with LLMs now. Making the API call is the small box, maybe even smaller than the one in that original diagram. Calling the model is the easy part. The hard part is everything around it: there is less stable ground truth, inputs are harder to constrain, outputs are non-deterministic, and the system is much easier for users to push in unintended directions. The hard part is giving teams the ability to iterate fast, trust that the system is working, and know that it is getting better. From our perspective, this is what an LLM platform actually has to solve for. It has to be reliable enough to support real product and operational workflows. It has to enable velocity, because many of the highest-leverage improvements are small changes that need to move quickly. And it has to create trust, so teams can understand output quality, catch regressions, and ship with confidence. These pillars reinforce each other. Reliability makes teams willing to depend on the platform in production. That production usage creates the data and feedback loops needed to build trust. And trust, in turn, makes it much easier for teams to move with velocity, because they can tell whether a change actually helped. The rest of this post is about those three strategic pillars and the concrete actions behind each of them that allowed us to build the LLM Platform at Whatnot. Figure 2: The LLM platform is organized around three self-reinforcing pillars: velocity, reliability, and trust, each supported by a distinct set of platform enablers.We built on foundations that were already there A big reason we were ## From Drift to Direction: The Architecture Behind Digression Control (Role Play) DevFeed: [From Drift to Direction: The Architecture Behind Digression Control (Role Play)]() Original publisher: [Read original article]() Author: Raka Dalal Published: 2026-04-09T12:25:37Z Content type: article Language: en Sources: [Udemy Engineering]() Topics: [Large Language Model](), [Artificial Intelligence](), [systems](), [Learning]() Tags: [ai](), [architecture](), [blocking](), [conversational-ai](), [data-science](), [large-language-models](), [learning](), [llm](), [llm-evaluation](), [prompt-engineering](), [systems]() ### AI overview Udemy's Role Play experience uses open-ended LLM-mediated conversations for practice with defined learning goals. The article examines digression as a systems challenge and introduces a Response Steering Layer intended to preserve conversational naturalness while improving goal adherence. ### Source excerpt Role Play @UdemyIntroduction AI-powered role play systems aim to recreate one of the most effective learning modalities: practicing real-world conversations in a safe, repeatable environment. In Udemy's Role Play experience, learners engage in open-ended, free-text dialogues with an AI character that embodies a specific persona -- such as an interviewer, a stakeholder, or a customer -- while working toward clearly defined learning goals. This contrasts with many traditional digital learning tools, which rely on static content, multiple-choice interactions, or scripted simulations that limit conversational variability. This design prioritizes realism and skill transfer, allowing learners to respond naturally rather than selecting from predefined options. Open-ended conversations mediated by LLM-based systems, however, introduce a core systems challenge: digression. In real conversations, people may drift off-topic, but human tutors actively manage and redirect such drift. In a learning-oriented role play mediated by an AI system, persistent digressions undermine the experience in more subtle but consequential ways. Learners may shift into meta-conversations, attempt to override the role being played, or explore tangents unrelated to the scenario's objectives. When this happens, learning goals become harder to assess, feedback loses grounding in the conversation, and the interaction no longer reflects the real-world situation the learner is meant to practice. A naive solution is to tightly constrain the conversation -- blocking inputs, rejecting turns, or forcing the dialogue back onto a scripted path. While this approach improves goal adherence, it comes at the cost of immersion and learner agency. Overly rigid controls make the AI feel less like a conversational partner and more like an instructional interface. On the other hand, allowing unrestricted conversational freedom preserves naturalness but leads to goal drift, inconsistent learning signals, and reduced reliab ## AI Agent Evaluation DevFeed: [AI Agent Evaluation]() Original publisher: [Read original article]() Author: Antonio Castelli Published: 2026-01-21T13:11:23Z Content type: tutorial Language: en Sources: [Booking.com Data Science]() Topics: [LLM evaluation / benchmarking](), [AI Agent](), [Artificial Intelligence](), [Tool]() Tags: [agent](), [ai](), [ai-agent](), [api](), [best-practices](), [blog-posts](), [evaluation](), [llm-agents](), [llm-evaluation](), [machine-learning](), [sql](), [tools]() ### AI overview Booking.com's article presents practical guidance for evaluating LLM agents. It explains how agents differ from single LLMs because they can use external tools and iterate through thoughts, actions, and observations, then introduces black-box and glass-box evaluation approaches. ### Source excerpt AI Agent Evaluation: practical tips at Booking.com Authors: Zeno Belligoli, Antonio Castelli, George Chouliaras This article is the 2nd part of our GenAI evaluation best practices series. You can read the first part, focusing on single LLM evaluation, here. 1. Overview LLM agents are advanced AI systems that leverage large language models to perform complex tasks autonomously. Unlike single LLMs that respond to direct prompts, agents can break down problems, use external tools, and iterate on solutions through a series of thoughts, actions, and observations. This allows them to achieve goals that require more intricate planning, reasoning, and interaction with various environments. Agents can autonomously determine if tool utilization is necessary to fulfill a user's request. For example, they might employ: a calculator for mathematical operations, a flights API to retrieve available flights for a certain destination, or execute a SQL query to fetch information about an hotel reservation from a database. Figure1: Schematic view of an agent. The user might interact with the agent within a multi-turn conversation, asking it to perform various tasks (recommend vacation destinations, ask questions about a hotel etc.). The agent should try to complete the requested tasks abiding by a set of rules (or constraints) provided by the product specification (e.g. do not recommend properties outside the inventory). In performing the tasks the agent has freedom to use a certain set of tools, and it can interact with them via API requests. Tool examples typically range from general (e.g. calculate travel distance) to use case specific functionalities (e.g. retrieving information from a database). While these enhanced capabilities are fundamental to solve tasks which would be difficult (or even impossible) to solve with text generation only, they require a more complex evaluation process compared to evaluating a single "prompt-response" LLM. The evaluation of an agent's performance ## Evaluating Doximity GPT with Ground Truths and LLM Performance Metrics DevFeed: [Evaluating Doximity GPT with Ground Truths and LLM Performance Metrics]() Original publisher: [Read original article]() Author: Doximity Published: 2024-05-10T13:00:00Z Content type: article Language: en Sources: [Doximity]() Topics: [LLM evaluation / benchmarking](), [Large Language Model](), [Machine learning](), [AI Development]() Tags: [evaluation](), [hipaa](), [large-language-models-llms](), [llm-evaluation](), [machine-learning]() ### AI overview This article explains how Doximity evaluates its Doximity GPT medical writing assistant. It discusses using ground truths and golden datasets to establish baseline metrics, comparing contender models, and combining standard metrics, manual review, benchmarks, and LLM-assisted evaluation to assess relevance, coherence, factual accuracy, and ethical compliance. ### Source excerpt At Doximity, we go to great lengths to ensure the quality of our products aligns with the standards physicians require. Across various industries, Large Language Models (LLMs) have become the backbone of numerous applications, driving advancements in everything from natural language processing to automated content creation. As we continue to develop products that make use of these LLMs, the need for rigorous and comprehensive evaluation of their outputs has never been more critical. Strap in as we explore the process for evaluating our Doximity GPT product, Doximity's HIPAA-compliant medical writing assistant, focusing on the importance of using "ground truths" to establish baseline metrics and the relative performance of contender models. A Brief Overview of How LLMs Generate Outputs LLMs are trained on vast amounts of textual data in order to learn patterns, structures, and nuances of language. By processing this data, these models develop the ability to generate text that mimics human writing. The output generation process involves the model understanding the input prompt, enhancing its focus via a system prompt, running all of that through its learned information and constructing a coherent and contextually relevant response. While this capability makes LLMs incredibly versatile, it also introduces unique challenges in ensuring the outputs meet specific quality and accuracy standards. LLM Evaluation: What is it and Why Does it Matter? Accuracy, precision, and recall are standard metrics that help evaluate different aspects of classification model quality in machine learning, and these metrics are still incredibly important for LLM evaluation as well. Additional traditional methods include manual review and comparison against predefined benchmarks. However, as LLMs grow more complex, these methods become less feasible due to the sheer volume of outputs and the nuanced understanding required to assess them. In addition to standard machine learning performance metr