Evaluating Productivity Tools for Teams

How to choose the best LLM for your use case 𝟭. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 - Start with task-based benchmarking: Choose a shortlist of LLMs and run tests specific to your use case (e.g., generate product descriptions, summarize long documents, or extract key insights). - Use open benchmark platforms like Hugging Face’s Evaluation or proprietary in-house benchmarks tailored to your data. 𝟮. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗣𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝘃𝘀. 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗲𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 - If your use case requires specialized knowledge, consider models already fine-tuned for your industry (like healthcare or finance). - For more general tasks, evaluate popular pre-trained models (e.g., GPT-4, LLaMA, Mistral) to see if they perform well out-of-the-box. 𝟯. 𝗣𝗶𝗹𝗼𝘁 𝗦𝗲𝘃𝗲𝗿𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗮 𝗦𝗮𝗻𝗱𝗯𝗼𝘅 - Set up a controlled environment and test models under real-world conditions. Look for how they handle edge cases and whether they require significant prompt engineering. - Pay attention to the ease of fine-tuning if customization is needed. 𝟰. 𝗔𝘀𝘀𝗲𝘀𝘀 𝗠𝗼𝗱𝗲𝗹 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗮𝗻𝗱 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 - Check the support and community around each model. Open-source models like LLaMA have vibrant communities that offer quick help and resources. - Evaluate the ecosystem of tools (e.g., prompt optimization libraries, monitoring solutions, or integration plugins) that come with each model. 𝟱. 𝗣𝗹𝗮𝗻 𝗳𝗼𝗿 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝘀𝘁𝘀 - For enterprise use, factor in not just model performance but also long-term sustainability. This includes how often the model is updated, security patches, and total costs. - Consider if the LLM vendor provides good SLAs for managed services or if it’s better to host open-source models on your infrastructure to manage costs effectively. What tips do you have to share with all of us that worked well?

Booking.com released a guide on how they evaluate every single AI app they build. Evaluating LLMs in prod is a different game than evaluating traditional ML models. - Golden datasets matter: human-annotated data is still the foundation for building trustworthy judge-LLMs. Without reliable labels, automated evaluation breaks down. - Annotation protocols are key: whether you go with a single annotator (basic) or multiple with consensus/weights (advanced), the consistency of annotation directly impacts evaluation quality. - Judge-LLM can't be the same as target-LLM: a stronger LLM can be used to evaluate the outputs of another, allowing scalable and automated monitoring of GenAI systems. - Pointwise vs. comparative judges: pointwise scoring works for production monitoring, but comparative evaluation (A vs. B) often provides stronger signals for ranking and system improvement. - Automation + synthetic data are emerging directions: auto-prompt pipelines and synthetic golden datasets could significantly reduce the time and cost of judge-LLM development. Link to full article by Georgios Christos Chouliaras, Antonio Castelli and Zeno Belligoli : https://lnkd.in/g3-qWFhB ♻️ Share it with anyone who might benefit :) I regularly share AI Agents and RAG projects on my newsletter: https://lnkd.in/dqJDN2NE #AI #GenAI #LLMs

Evaluating LLMs is not like testing traditional software. Traditional systems are deterministic → pass/fail. LLMs are probabilistic → same input, different outputs, shifting behaviors over time. That makes model selection and monitoring one of the hardest engineering problems today. This is where Eval Protocol (EP) developed by Fireworks AI is so powerful. It’s an open-source framework for building an internal model leaderboard, where you can define, run, and track evals that actually reflect your business needs. → Simulated Users – generate synthetic but realistic user interactions to stress-test models under lifelike conditions. → evaluation_test – pytest-compatible evals (pointwise, groupwise, all) so you can treat model behavior like unit tests in CI/CD. → MCP Extensions – evaluate agents that use tools, multi-step reasoning, or multi-turn dialogue via Model Context Protocol. → UI Review – a dashboard to visualize eval results, compare across models, and catch regressions before they ship. Instead of relying on generic benchmarks, EP lets you encode your own success criteria and continuously measure models against them. If you’re serious about scaling LLMs in production, this is worth a look: evalprotocol.io

I've spent countless hours building and evaluating AI systems. This is the 3-part evaluation roadmap I wish I had on day one. Evaluating an LLM system isn't one task. It's about measuring the performance of each component in the pipeline. You don't just test "the AI"; You test the retrieval, the generation, and the overall agentic workflow. 𝗣𝗮𝗿𝘁 𝟭: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗧𝗵𝗲 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲) Your system is only as good as the context it retrieves. 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻: How much of the retrieved context is actually relevant vs. noise? ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗥𝗲𝗰𝗮𝗹𝗹: Did you retrieve all the necessary information to answer the query? ↳ 𝗡𝗗𝗖𝗚: How high up in the retrieved list are the most relevant documents? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: RAGAs Framework (Repo) https://lnkd.in/gAPdCRzh ↳ 𝗣𝗮𝗽𝗲𝗿: RAGAs Paper https://lnkd.in/gUKVe4ac 𝗣𝗮𝗿𝘁 𝟮: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗟𝗟𝗠'𝘀 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲) Once you have the context, how good is the model's actual output? 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗙𝗮𝗶𝘁𝗵𝗳𝘂𝗹𝗻𝗲𝘀𝘀: Does the answer stay grounded in the provided context, or does it start to hallucinate? ↳ 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Is the answer directly addressing the user's original prompt? ↳ 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴: Did the model adhere to the output format you requested? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲: LLM-as-Judge Paper https://lnkd.in/gyhaU5CC ↳ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: OpenAI Evals & LangChain Evals https://lnkd.in/g9rjmfGS https://lnkd.in/gmJt7ZBa 𝗣𝗮𝗿𝘁 𝟯: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 (𝗧𝗵𝗲 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗦𝘆𝘀𝘁𝗲𝗺) Does the system actually accomplish the task from start to finish? 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲: Did the agent successfully achieve its final goal? This is your north star. ↳ 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Did it call the correct tools with the correct arguments? ↳ 𝗖𝗼𝘀𝘁/𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗽𝗲𝗿 𝗧𝗮𝘀𝗸: How many tokens and how much time did it take to complete the task? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗗𝗞 𝗗𝗼𝗰𝘀: https://lnkd.in/g2TpCWsq ↳ 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴(.)𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗘𝘃𝗮𝗹 𝗖𝗼𝘂𝗿𝘀𝗲: https://lnkd.in/gcY8WyjV Stop testing your AI like a monolith. Start evaluating the components like a systems engineer. That's how you build systems that you can actually trust. Save this roadmap. What's the hardest part of your current eval pipeline? ♻️ Repost this to help your network build better systems. ➕ Follow Shivani Virdi for more.

How Do You Actually Measure LLM Performance- A Practical Evaluation Framework for 2025 As LLMs continue to shape enterprise AI, measuring their performance requires more than checking if the answer is “correct.” Modern evaluation spans accuracy, semantics, safety, efficiency, and human judgment. 🔍 1. Accuracy Metrics ◾ Perplexity (PPL) – How well the model predicts text (lower = better) ◾Cross-Entropy Loss – Measures prediction quality during training 📌 Useful for benchmarking probabilistic models. 🔤 2. Lexical Similarity Metrics ◾BLEU – n-gram precision ◾ROUGE (N, L, W) – n-gram recall & sequence matching ◾METEOR – Considers synonyms, stemming, word order 📌 Good for summarization and translation, but limited in capturing meaning. 🧠 3. Semantic Similarity Metrics ◾BERTScore – Uses contextual embeddings for semantic alignment ◾MoverScore – Measures semantic distance 📌 Closer to human judgment than word-based scores. 📝 4. Task-Specific Metrics ◾Exact Match (EM) – Perfect match with expected answer ◾F1 Score – Partial match overlap 📌 Ideal for QA, extraction, and structured outputs. ⚖️ 5. Bias & Fairness Metrics ◾Bias Score ◾Fairness Score 📌 Critical for high-stakes AI use cases: finance, justice, healthcare. ⚡ 6. Efficiency Metrics ◾Latency ◾Resource Utilization 📌 Required for production-grade, scalable systems. 🤝 7. Human Evaluation ◾Fluency ◾Coherence ◾Relevance ◾Toxicity & Bias 📌 Still the gold standard—automated metrics cannot fully capture nuance. 💡 Final Takeaway A robust LLM evaluation framework must combine: ◾Accuracy + Semantic Understanding + Safety + Efficiency + Human Judgment. ◾This multi-layered approach ensures trustworthy, high-performance AI systems that work reliably in production. Reference: “How to Measure LLM Performance,” Analytics Vidhya (document provided). #LLMEvaluation #AIProductManagement #GenerativeAI #MachineLearning #AIEthics #ModelEvaluation #RAG #NLP #ArtificialIntelligence #LLM #AIinBusiness #AIMetrics #DataScience #MLOps #ResponsibleAI

Most teams pick an LLM based on vibes and benchmarks. Both will fail you in production. The 9-point LLM production checklist 1. P50 / P95 latency under real load Don't test cold. Simulate concurrent users. A model that's fast at 1 req/s often chokes at 50. Measure time-to-first-token separately — it dominates perceived speed. Target: P95 TTFT< 1.5s for chat, < 500ms for autocomplete 2. True cost per 1M tokens (input + output) Providers quote input prices. Your app is mostly output tokens. Model your actual input/output ratio — most apps run 1:3 or worse. Factor in caching, batching, and reserved throughput tiers. Red flag: any estimate that ignores output-heavy workloads 3. Context fidelity (lost-in-the-middle test) Bury a critical fact at position 40% of your max context. Ask the model to retrieve it. Most models degrade sharply for content that isn't at the start or end of a long context window. Target:>90% recall across all context positions 4. Hallucination rate on your domain Generic hallucination evals don't predict your failure mode. Build 50 domain-specific prompts where the correct answer is "I don't know." Count confident wrong answers. This number will surprise you. Target:<2% confident hallucinations on your eval set 5. Refusal rate on legitimate queries Over-refusal is a silent killer of user trust. Test edge-case but totally valid prompts in your domain — medical, legal, financial, security. High refusal rates on real use cases = high churn. Target:<3% false refusal on a representative query set 6. Tool use / function call reliability Ask the model to call a tool correctly across 100 prompts with varied phrasing. Check: correct tool selected, right arguments extracted, no hallucinated parameters. Parallel tool calls are a separate test. Target:>95% correct tool selection + arg extraction 7. Instruction-following consistency Give the model a system prompt with 5 constraints. Track how many it violates across 200 generations. Models that "mostly" follow instructions are unpredictable at scale — edge cases ship to prod. Target:<1% constraint violation rate 8. Output format stability If you're parsing structured output (JSON, XML, markdown tables), stress test it. Rephrasing the same prompt 50 ways and checking format compliance will reveal how brittle the model is without schema enforcement. Target:>98% valid structure without retries 9. Regression stability across model updates Ask your provider's update policy. Does the model change silently? Do you get versioned endpoints? A model that's great today and 10% worse next Tuesday because of a silent update is a production incident waiting to happen. Non-negotiable: pinned versioned endpoints in prod The trap most teams fall into: they evaluate on quality metrics only, then get surprised by cost overruns, latency spikes, or refusals in prod. Run this checklist before you commit. Change models after the fact and you're rewriting prompts, evals, and half your integration layer.

I've spent 10+ years fixing failed AI deployments in huge companies like Microsoft. Here are 8 systematic checks that serious teams always run: 1. Redundancy Hallucinations are obvious. Repetition is sneakier. LLMs love to circle phrases ("in conclusion," "it's important to note"). Good evals catch these loops - because in ops, wasted words = wasted trust. -- 2. Compression A 1,000-word summary isn't a summary. Strong evals ask: "Can this be cut by 20% without losing meaning?" If the answer is yes, the model isn't doing its job. -- 3. Factual drift The most dangerous failure mode isn't hallucination. It's a summary that sounds accurate but quietly drops or twists a fact. Evaluations run line-by-line cross-checks against the source to prevent silent errors. -- 4. Ordering logic Rankings feel authoritative - but are they? Teams check whether "top recommendations" are actually ordered by a consistent signal, not random chance. -- 5. Tone alignment Ops work is often client-facing. A perfectly accurate draft that sounds robotic or defensive can still tank trust. Evals measure tone against real examples of acceptable communication. -- Consistency 6. One example might look good. Ten might not. Teams run tests across batches to see if tags, categories, or structures hold steady under variation. -- 7. Cost-to-value Eval isn't just about output quality. It's about ROI. If the token bill doubles, does the output double in usefulness? If not, downgrade the model or trim context. -- 8. Latency-to-utility Speed isn't everything. But if an answer takes 18 seconds and users only wait for 6, quality is irrelevant. Latency evals don't measure time; they measure patience thresholds. -- The difference between "it looks fine" and "it works every time" is eval discipline. These checks are how good teams turn LLMs into dependable systems. 🚀 P.S. Want more evaluation frameworks like this? We share systematic testing approaches and reliability playbooks every week in our free AI Product Accelerator community. ↪️ Link in the comments. 100% free to join.

Reliable evaluation methods for Large Language Model (LLM)-based agents are crucial, yet rapidly evolving. In my view, traditional LLM benchmarks fall short because they don’t fully capture the unique capabilities agents bring—such as planning, reasoning, tool use, self-reflection, memory, and interaction with dynamic environments. Here's how I see current evaluation methods shaping up across four key areas: 1. Agent Capabilities Evaluation: - Planning & Multi-Step Reasoning: Benchmarks like GSM8K, HotpotQA, ARC, and frameworks such as ToolEmu and PlanBench effectively test an agent's ability to decompose tasks, reason causally, and correct its own errors. - Function Calling & Tool Use: Evaluations are evolving from basic tasks (ToolBench) toward complex interactions (ComplexFuncBench, NESTFUL). - Self-Reflection: Benchmarks like LLF-Bench and LLM-Evolve help gauge how agents learn from their past actions and feedback. - Memory: Tools such as MemGPT and LoCoMo are valuable for assessing context retention, while StreamBench and Reflexion highlight continuous learning and effective action planning. 2. Application-Specific Agent Evaluation: - Web Agents: Moving from simple simulations (MiniWob) to complex and realistic environments (WebArena, WorkArena++). - Software Engineering Agents: Practical benchmarks like SWE-bench and SWELancer are critical for measuring an agent’s capability in real-world software development scenarios. - Scientific Agents: Platforms like ScienceWorld and CORE-Bench are effective for evaluating an agent's proficiency in research tasks such as literature synthesis and experiment design. - Conversational Agents: Realistic dialogues and user simulations (MultiWOZ, ALMITA) provide a reliable way to test conversational skills. 3. Generalist Agent Evaluation: - Tools such as GAIA, TheAgentCompany, and CRMArena effectively assess agents across diverse, real-world scenarios. 4. Evaluation Frameworks: - LLMOps tools are indispensable for detailed evaluation, real-time monitoring, and identifying areas for improvement during agent development and deployment. Trends & Future Directions: - Evaluations are increasingly moving towards dynamic, realistic, and challenging environments. - Regular updates to benchmarks are essential to keep up with rapid technological advancements. - Future research should prioritize granular, step-by-step metrics, consider efficiency and cost implications, develop automated evaluation strategies, and rigorously assess safety, compliance, and bias. Addressing these gaps is crucial for responsible and effective deployment of LLM-based agents in real-world applications.

As a strategic AI leader, you don’t need to be in the weeds implementing generative AI (GenAI) systems—but you do need to understand the key patterns shaping their success. Your role is to empower your teams as they push the boundaries of what’s possible—often challenging the status quo in legal, privacy, and security. By understanding these emerging techniques, you’ll be better equipped to advocate for responsible AI adoption, remove roadblocks, and position your organization at the forefront of AI innovation. Here’s a byte-sized breakdown of the key insights from Martin Fowler latest thought-provoking article, where he unpacks the emerging patterns shaping the future of GenAI in production. 1. Direct Prompting - LLMs are only as good as their training data and lack real-time updates. - They can hallucinate, mislead, or respond with overconfidence. - Needs enhancements like retrieval augmentation or fine-tuning to be reliable. Action: Don’t rely on raw LLM outputs—use techniques like Retrieval-Augmented Generation (RAG) or guardrails to ensure reliability. 2. Evals - Unlike traditional deterministic software testing, LLM evals require scoring mechanisms. - Methods include self-evaluation, LLM-as-a-judge, and human evaluation (best results come from a mix of these). Benchmarking helps track performance over time and assess upgrades. Action: Integrate automated evals into your development pipeline to maintain model quality. 3. Embeddings - Transforms text, images, and other unstructured data into numerical vectors. - Enables semantic search, similarity detection, and retrieval of relevant content. - More efficient than traditional keyword-based searches. Action: Use embeddings to structure unstructured data and improve LLM-driven search capabilities. 4. Retrieval-Augmented Generation (RAG) - Avoids fine-tuning costs by supplying relevant context dynamically. - Retrieves relevant document fragments before generating responses. - Improves accuracy and reduces hallucinations. Action: Implement RAG instead of fine-tuning when dealing with dynamic or domain-specific knowledge. 5. Hybrid Retrieval - Embedding-based search is powerful but loses semantic nuance. - Keyword search (TF-IDF, BM25) complements embeddings for better retrieval. - Used in complex, large-scale information retrieval systems. Action: Use hybrid retrieval for more precise and relevant document fetching in RAG. 6. Query Rewriting - LLMs generate multiple variations of a query to capture different nuances. - Helps retrieve better documents when user queries are vague. Action: Implement query rewriting for better document retrieval, especially when users aren’t precise in their queries. What You Can Do Next: ✅ Integrate RAG for real-world AI applications. ✅ Set up evals to continuously measure LLM performance. ✅ Experiment with query rewriting and rerankers to improve AI search accuracy. ✅ Implement guardrails to prevent AI misuse.

𝐘𝐨𝐮𝐫 𝐋𝐋𝐌 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐢𝐬 𝐯𝐢𝐛𝐞𝐬 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐩𝐫𝐞𝐚𝐝𝐬𝐡𝐞𝐞𝐭. Run a few samples, read the outputs, nod, ship. The more rigorous version: run those outputs through another LLM and ask "is this good?" That's not evaluation. That's asking one black box to grade another. A benchmark tells you what a model is capable of. A test suite tells you whether your system is behaving correctly. Almost every team is answering the first question when they need to be answering the second. Deterministic assertions catch this before any LLM judge runs: → Response arrives within latency threshold → Output matches expected schema, required fields present, types correct → No PII in the response payload → Output length within acceptable range → No content from a blocked category None of these require a model to evaluate. A JSON schema check runs in microseconds. These are pass or fail, they run on every output, and they produce a log you can audit. LLM-as-judge has one legitimate job: evaluating semantic quality where correctness is genuinely ambiguous - tone, coherence, relevance. That's the residual after deterministic checks clear. It should cover 20% of your eval surface, not 100%. The other problem: LLM judges have documented biases. They prefer longer responses. They prefer their own outputs when used for self-evaluation. They're sensitive to prompt order. Using one as your primary eval layer produces noisy signal in ways you cannot fully characterize. The eval stack that works: 1. Deterministic assertions on every output, in CI, on every deploy 2. Regression set of known inputs with expected outputs - drift fails the build 3. LLM-as-judge scores semantic quality on a sampled subset 4. Human review reserved for edge cases and new failure categories This is a test pyramid. Standard software engineering for 30 years. The AI field is relearning it from scratch. When your model gets updated, fine-tuned, or swapped - and it will - you need a test suite that catches regressions in under five minutes, not a leaderboard score. 𝐸𝑦𝑒𝑏𝑎𝑙𝑙𝑖𝑛𝑔 𝑜𝑢𝑡𝑝𝑢𝑡𝑠 𝑖𝑠 𝑛𝑜𝑡 𝑒𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛. 𝐼𝑡'𝑠 ℎ𝑜𝑝𝑖𝑛𝑔.