GenAI project evaluation framework

McKinsey & Company 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟭𝟱𝟬+ 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝗳𝗼𝘂𝗻𝗱 𝗼𝗻𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗵𝗿𝗲𝗮𝗱: ⬇️ One-off solutions don’t scale. The most successful projects take a different path: They use open, modular architectures that enable speed, reuse, and control. → Designed for reuse → Able to plug in best-in-class capabilities → Free from vendor lock-in This is the reference architecture McKinsey now recommends — optimized to scale what works while staying compliant. It consists of five core components: ⬇️ 𝟭. 𝗦𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹: → A secure, compliant “pane of glass” where teams can launch, monitor, and manage GenAI apps. → Preapproved patterns, validated capabilities, shared libraries. → Observability and cost controls built-in. 𝟮. 𝗢𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 → Services are modular, reusable, and provider-agnostic. → Core functions like RAG, chunking, or prompt routing are shared across apps. → Infra and policy as code, built to evolve fast. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 → Every prompt and response is logged, audited, and cost-attributed. → Hallucination detection, PII filters, bias audits — enforced by default. → LLMs accessed only through a centralized AI gateway. 4. 𝗙𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Centralized logging, analytics, and monitoring across all solutions → Built-in lifecycle governance, FinOps, and Responsible AI enforcement → Secure onboarding of use cases and private data controls → Enables policy adherence across infrastructure, models, and apps 5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 → Modular setup for user interface, business logic, and orchestration → Integrated agents, prompt engineering, and model APIs → Guardrails, feedback systems, and observability built into the solution → Delivered through the AI Gateway for consistent compliance and scale The message is clear: If your GenAI program is stuck, don’t look at the LLM. Look at your platform. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

Everyone's talking about prompts, fine-tuning, and LLMs —  But what separates a 𝗱𝗲𝗺𝗼 from a 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗚𝗲𝗻𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻? I created this infographic to break it down into 12 actionable, end-to-end stages — from the first line of planning to post-deployment scaling. Here’s how high-performing GenAI teams approach it: ↳ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 — What’s the 𝘳𝘦𝘢𝘭 goal? Not just “chat with PDF.”   ↳ 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 — Garbage in = garbage out.   ↳ 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 — GPT? LLaMA? Mixtral? Pick wisely.   ↳ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 & 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 — Customize to your domain.   ↳ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 — It’s not magic, it’s design.   ↳ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 — Don’t ship without this.   ↳ 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗥𝗲𝘃𝗶𝗲𝘄 — Biases don’t go away on their own.   ↳ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 → 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 → 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 — The real work begins 𝘢𝘧𝘵𝘦𝘳 launch. Whether you’re building internal copilots, intelligent assistants, or complex multi-agent systems — this structured approach keeps things scalable, safe, and aligned with business value. 𝗪𝗵𝗶𝗰𝗵 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝘀𝘁𝗮𝗴𝗲𝘀 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗺𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝘀𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝗼𝗿 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵?

Confusion matrix for GenAI? Yes, you read that right. We all know how confusion matrices work for classification problems. But here's the thing: GenAI doesn't fit into neat boxes. A model can: ✅ Sound confident ✅ Write beautifully  ❌ And still hallucinate It can be factually correct but utterly useless. Safe but unhelpful. So I reimagined the confusion matrix for GenAI evaluation. Instead of just "right or wrong," we now map two dimensions: → Faithfulness vs. Utility This gives us four quadrants: 1. Ideal = correct and useful 2. Helpful but hallucinated = sounds good, but wrong 3. On-topic but not helpful = accurate but useless 4. Garbage = neither correct nor useful From here, we get metrics that actually matter: ✅ Faithfulness Precision → When it says something, how often is it right? ✅ Coverage Recall → Of what it should say, how much did it cover? ✅ Hallucination Rate → How often does it make things up? The carousel breaks down 3 more evaluation frameworks to use: ➢ Readability vs. Informativeness ➢ Safety vs. Helpfulness ➢ Coherence vs. Novelty Each one maps different quality dimensions for your GenAI outputs. What's the biggest challenge you face when evaluating your GenAI outputs: hallucinations, irrelevance, or something else entirely? Follow me, Bhavishya Pandit, for practical GenAI insights from the trenches 🔥

Metrics won’t save your GenAI project. Leadership will. I made a full playbook for designing, measuring, and leading GenAI initiatives: https://lnkd.in/eMCdaXJF It covers questions most teams skip: ♦️ How do you do capability decomposition? ♦️ How do you make a golden set? ♦️ Should you use a single human rater? (For test-retest screening, yes. For your eval pipeline, no.) ♦️ How do you avoid measuring yourself into polished inefficiency? ♦️ What does it take to build #genai #metrics that actually reduce uncertainty instead of trying to win philosophical arguments? I walk through 8 steps—from clarifying purpose, to operationalizing abstract goals, to curating golden sets, to using human #raters responsibly. But the deeper point is this: metrics are leadership choices. They’re tools for steering, not trophies for display. If you’re building or overseeing GenAI systems, this is a guide you’ll want at hand. 🔗 https://lnkd.in/eMCdaXJF

𝐈𝐬 𝐘𝐨𝐮𝐫 𝐆𝐞𝐧𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐞𝐝 𝐟𝐨𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐨𝐫 𝐉𝐮𝐬𝐭 𝐂𝐚𝐥𝐥𝐢𝐧𝐠 𝐚𝐧 𝐀𝐏𝐈? Calling an LLM API is easy. Building a GenAI system that's fast, reliable, secure, and affordable at scale is real engineering. Here are the 10 principles that separate a prototype from production. 1. API Gateway: Single entry point managing all incoming requests. Authentication, rate limiting, routing all before anything touches your models. One controlled front door. 2. Load Balancing: Distributes traffic across multiple inference servers to prevent overload. Essential the moment demand exceeds what a single instance handles. 3. Caching Layer: Stores previously computed responses, embeddings, and prompts. Cache hit returns instantly. Cache miss runs inference and stores the result. The cheapest performance win why recompute an answer you already have? 4. Model Serving: Infrastructure that runs and scales AI models in production. The layer that turns "a model" into "a service that scales." 5. Vector Database: Stores embeddings for semantic search. The backbone of RAG and anything that finds by meaning, not keywords. 6. Queue and Async Processing: Handles long-running tasks without blocking users. Keeps the system responsive even when individual tasks take time. 7. Observability and Monitoring: Tracks performance, latency, failures, token usage, model behavior. You can't operate what you can't see and token usage is a cost line you must watch. 8. Security and Guardrails: Input validation, safety filters, output moderation. Guards at both entrance and exit because either can be a vulnerability. 9. Reliability and Resilience: Things will fail. Resilience means failing gracefully instead of catastrophically. 10. Cost Optimization: At scale, picking the right model per request and trimming tokens is the difference between profit and loss. How do they work together? Control the entry (gateway). Spread the load (balancing). Cache aggressively (caching). Serve smartly (model serving). Store by meaning (vector DB). Process async (queues). Observe everything (monitoring). Guard both ends (security). Plan for failure (resilience). Watch the bill (cost). Production GenAI is systems engineering. Get these 10 right and your system scales without falling over. Which principle is your system weakest at? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) for more PS: Found this useful? Join 3,000+ AI architects and engineering leaders from Microsoft, Google, IBM, PwC and others reading my weekly newsletter 𝗗𝗶𝗮𝗿𝘆 𝗼𝗳 𝗮𝗻 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁. I break down real enterprise AI systems, agentic patterns, and what actually works in production. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #SystemDesign #AIEngineering

If you’ve ever shipped a GenAI model to production, you already know the real interview isn’t about transformers, it’s about everything that breaks the moment real users touch your system. 1) How would you evaluate an LLM powering a Q&A system? Approach: Don’t talk about accuracy alone. Break it down into: ✅ Functional metrics: exact match, F1, BLEU, ROUGE depending on task. ✅ Safety metrics: hallucination rate, refusal rate, PII leakage. ✅ User-facing metrics: latency, token cost, answer completeness. ✅ Human evaluation: rubric-based scoring from SMEs when answers aren’t deterministic. ✅ A/B tests: compare model variants on real user flows. 2) How do you handle hallucinations in production? Approach: ✅ Show you understand layered mitigation: ✅ Retrieval first (RAG) to ground the model. ✅ Constrain the prompt: citations, “answer only from provided context,” JSON schemas. ✅ Post-generation validation like fact-checking rules or context-overlap checks. ✅ Fall-back behaviors when confidence is low: ask for clarification, return source snippets, route to human. 3) You’re asked to improve retrieval quality in a RAG pipeline. What do you check first? Approach: Walk through a debugging flow: ✅ Check document chunking (size, overlap, boundaries). ✅ Evaluate embedding model suitability for domain. ✅ Inspect vector store configuration (HNSW params, top_k). ✅ Run retrieval diagnostics: is the top_k relevant to the question? ✅ Add metadata filters or rerankers (cross-encoder, ColBERT-style scoring). 4) How do you monitor a GenAI system after deployment? Approach: ✅ Make it clear that monitoring isn’t optional. ✅ Latency and cost per request. ✅ Token distribution shifts (prompt bloat). ✅ Hallucination drift from user conversations. ✅ Guardrail violations and safety triggers. ✅ Retrieval hit rate and query types. ✅ Feedback loops from thumbs up/down or human review. 5) How do you decide between fine-tuning and using RAG? Approach: ✅ Use a decision tree mentality: ✅ If the issue is knowledge freshness, go with RAG. ✅ If the issue is formatting/style, go with fine-tuning. ✅ If the model needs domain reasoning, consider fine-tuning or LoRA. ✅ If the data is large and structured, use RAG + reranking before touching training. Most interviews test what you know. GenAI interviews test what you’ve survived. Follow Sneha Vijaykumar for more... 😊 #genai #datascience #rag #production #interview #questions #careergrowth #prep

By now, the "95% failure rate" of GenAI financial returns (ref MIT's Project NANDA) is part of all consulting decks. The report blames the incorrect approach as the primary reason, rather than model maturity, etc. The key is to understand what #ROI metrics are used to determine the financial returns. I asked #Copilot on this, and here's what it told me: --- Here are three examples of ROI frameworks that enterprises are using to evaluate and scale GenAI adoption effectively: 1. Business Outcome-Based ROI Framework (Gartner) Summary: Gartner recommends aligning GenAI initiatives with measurable business outcomes such as cost reduction, revenue growth, or productivity gains. For example, a retail company using GenAI for automated product descriptions tracked a 22% increase in conversion rates and a 15% reduction in content creation costs. The framework emphasizes setting baseline metrics before deployment and tracking improvements post-implementation. 🔗 https://lnkd.in/dER7cTeF 2. Time-to-Value and Efficiency Metrics (BCG) Summary: Boston Consulting Group suggests using time-to-value (TTV) and operational efficiency as key ROI indicators. In one case, a logistics firm used GenAI to optimize routing, reducing delivery times by 18% and fuel costs by 12%. BCG’s framework includes pre/post comparisons, automation impact, and employee productivity metrics to quantify GenAI’s contribution. 🔗 https://lnkd.in/da2zcSfW 3. Model Performance vs. Business KPIs (McKinsey) Summary: McKinsey advocates for linking GenAI model performance directly to business KPIs. For instance, a financial services firm used GenAI for customer support automation and tracked resolution time, customer satisfaction scores, and call deflection rates. The framework includes continuous monitoring of model accuracy, relevance, and business impact. 🔗 https://lnkd.in/dA6zEGuS 🔑 Key Message Summary Effective GenAI ROI frameworks combine technical performance metrics with business impact indicators. Leading approaches include tracking cost savings, productivity gains, time-to-value, and alignment with strategic KPIs. Enterprises that define success upfront and monitor outcomes continuously are more likely to scale GenAI successfully. --- The direction taken seems to be well-intentioned. However, the measure of success is not quite what might lead to real solid business outcomes! Individual productivity improvements are just that! They don't scale across the organization unless "vertically scaled" top-to-down an entire process delivering bottomline improvements, which then need to be further "horizontally scaled" end-to-end across the entire value chain of the firm to deliver topline value! My forthcoming book on Cognitive Chasm provides actionable guidance to practitioners on this.

I’ve noticed a pattern in conversations with leaders: while there’s a lot of enthusiasm around GenAI, many feel their investments aren’t yielding the impact they expected. I think one reason for this is that many organizations aren’t clear on where GenAI truly fits within their business. At LatentView Analytics, we created the RAISE framework to bridge this gap and help organizations move from GenAI vision to GenAI reality with measurable impact. Here’s the breakdown: 1️⃣ Retrieve - Enhances knowledge discovery and data retrieval, so you have the insights you need at your fingertips. 2️⃣ Analyze - Summarizes data for actionable insight generation, enabling quicker, smarter decision-making. 3️⃣ Implement - Supports automation with AI agents, freeing teams to focus on strategic work. 4️⃣ Sync & Execute - Manages complex workflows, with multi-agent collaboration driving seamless operations across functions. The path to GenAI success isn’t just having advanced tools—it’s about knowing how to embed them where they’ll actually make a difference. Using our RAISE framework, you can classify the right business areas for GenAI implementation. We categorize our bespoke GenAI solutions with this framework, enabling leaders to make informed decisions. With the right approach, GenAI can deliver powerful results across different functions. The equation is simple (even if the execution isn’t): a clear vision + a solid data strategy = profitable GenAI. Together, let’s rAIse the bar for what is possible through GenAI! #GenAI #RAISEframework #LatentView

Recently, I've been building a GenAI product and wanted to understand how to properly evaluate AI agents. This led me to take the "Evaluating AI Agents" course from deeplearning.ai. For anyone in my position, here are the key concepts to know: AI Agent Architecture • Router: The "brain" deciding which function to call • Skills: Logic blocks that perform specific tasks • Memory/State: Storage for context and execution logs Evaluation Techniques • Code-Based Evaluations: Traditional testing comparing outputs to expected results • LLM as a Judge: Using another AI to evaluate outputs based on criteria • Human Annotations: The gold standard, but resource-intensive What to Evaluate • Router: Did it select the correct function with the right parameters? • Skills: Are individual components performing correctly? • Path Convergence: Does the agent reach a satisfactory conclusion? Evaluation-Driven Development This powerful approach transformed my building process: • Curate test cases representing typical inputs • Run experiments with different agent variations • Apply evaluators to compare results • Create a continuous improvement flywheel As I continue building, this evaluation framework has been invaluable for improving reliability. What evaluation methods have you found effective for your AI systems? #ArtificialIntelligence #GenAI #MachineLearning #AIAgents #Evaluation

I’ve noticed that many GenAI application projects put in automated evaluations (evals) of the system’s output probably later — and rely on humans to manually examine and judge outputs longer — than they should. This is because building evals is viewed as a massive investment (say, creating 100 or 1,000 examples, and designing and validating metrics) and there’s never a convenient moment to put in that up-front cost. Instead, I encourage teams to think of building evals as an iterative process. It’s okay to start with a quick-and-dirty implementation (say, 5 examples with unoptimized metrics) and then iterate and improve over time. This allows you to gradually shift the burden of evaluations away from humans and toward automated evals. I wrote previously in The Batch about the importance and difficulty of creating evals. Say you’re building a customer-service chatbot that responds to users in free text. There’s no single right answer, so many teams end up having humans pore over dozens of example outputs with every update to judge if it improved the system. While techniques like LLM-as-judge are helpful, the details of getting this to work well (such as what prompt to use, what context to give the judge, and so on) are finicky to get right. All this contributes to the impression that building evals requires a large up-front investment, and thus on any given day, a team can make more progress by relying on human judges than figuring out how to build automated evals. I encourage you to approach building evals differently. It’s okay to build quick evals that are only partial, incomplete, and noisy measures of the system’s performance, and to iteratively improve them. They can be a complement to, rather than replacement for, manual evaluations. Over time, you can gradually tune the evaluation methodology to close the gap between the evals’ output and human judgments. For example: - It’s okay to start with very few examples in the eval set, say 5, and gradually add to them over time — or subtract them if you find that some examples are too easy or too hard, and not useful for distinguishing between the performance of different versions of your system. - It’s okay to start with evals that measure only a subset of the dimensions of performance you care about, or measure narrow cues that you believe are correlated with, but don’t fully capture, system performance. For example if, at a certain moment in the conversation, your customer-support agent is supposed to (i) call an API to issue a refund and (ii) generate an appropriate message to the user, you might start off measuring only whether or not it calls the API correctly and not worry about the message. Or if, at a certain moment, your chatbot should recommend a specific product, a basic eval could measure whether or not the chatbot mentions that product without worrying about what it says about it. [Truncated due to length limit. Full text: https://lnkd.in/gygj3y7w ]