TecAce

AI Supervision 10 — The Blueprint for RAG Success: a practical guide to integrating evaluation into your Retrieval-Augmented Generation architecture. Learn how to map retrieved documents to validated answers and design a seamless Retrieve-Generate-Evaluate pipeline. Read more: https://wix.to/Bpc5qKG

AI Supervision 10. The Blueprint for RAG Success: Integrating AI Supervision into Your Architecture "We built a RAG system, but where exactly does the evaluation tool fit in?" "How do we map the retrieved documents to the actual answer for validation?" The final puzzle piece in LLM service development is Architecture. It’s not just about calling an API; it’s about creating a seamless pipeline ...

Many AI evaluation tools stay trapped in the browser while real users interact via mobile apps, Slack, or backend workflows. AI Supervision closes that gap with SDKs and mobile integration — enabling evaluation in the same contexts where your AI runs. Dive into practical approaches and benefits: https://wix.to/fkDHFuG

AI Supervision 9. AI Beyond the Web: Seamless Evaluation with SDKs and Mobile Integration "Our AI chatbot lives in a mobile app. Do we have to test it on a separate web dashboard?" "Copy-pasting logs from our server to the evaluation tool is tedious." Many AI evaluation tools are stuck in the browser sandbox. However, real users interact with AI in mobile apps, internal messengers like S...

Decisions about switching LLMs or tuning prompts hinge on granular metrics—not just averages. Our blog post (2-minute read) compares GPT and Claude with trend analysis and detailed result breakdowns to help you assess quality vs. cost trade-offs. Read it here: https://wix.to/OvCXJA7

AI Supervision 8. GPT vs. Claude? Stop Guessing: Precision Model Comparison & Trend Analysis "I tweaked the prompt, but now the answers feel weird." "I want to switch to a cheaper model, but I'm scared the quality will drop." AI development is a constant series of Trade-offs. You have to decide whether to switch models, adjust prompts, or tune RAG settings. However, looking at just the "Ave...

Teams shipping AI to users face two hidden killers: latency and operational cost. Our latest blog shows how the AI Supervision Real-time Insights Dashboard helps you visualize throughput, latency, and cost so you can optimize model routing and runtime decisions. Read the full post: https://wix.to/rptvkKX

AI Supervision 7. Cut Costs, Boost Speed: Mastering the Real-time Insights Dashboard "Why is our API bill so high this month?" "The answer quality is great, but it's too slow for users to wait." For AI development teams, the challenges don't end with "Accuracy." As a service approaches commercialization, it hits the realistic barriers of Latency and Operational Cost. Even a high-qua...

If your evaluation datasets live across Slack, local drives, and fuzzy filenames like “real_final.xlsx,” your model metrics aren’t trustworthy. Our latest blog outlines how to implement a centralized TestSet Management System with AI Supervision so teams can manage test cases systematically and reproduce results. Read the short guide: https://wix.to/f7phHmH

AI Supervision 6. No More 'test_final_v2.xlsx': Mastering Systematic TestSet Management "Where is the dataset we used for the last evaluation?" "Is the file Dave sent the latest version?" As you develop AI models, evaluation data files tend to scatter across Slack channels and local drives, with filenames evolving into chaos like v1, final, real_final. If your data isn't managed, your....

Manual test creation is a bottleneck for AI teams. TC Generator automates TestSet generation with diverse Q&A pairs, reducing bias and surfacing edge cases. Read our concise guide and streamline your evaluation pipeline. https://wix.to/8iFuB6W

AI Supervision 5. Stop Writing Manual Tests! Master AI Evaluation with TC Generator "I need a dataset of Q&A pairs to evaluate my model, but creating it is a nightmare." For many AI Engineers and PMs, the biggest bottleneck isn't developing the model—it's creating the TestSet. Staring at a blank spreadsheet and manually inventing hundreds of questions is not only inefficient but ...

New post from TecAce: AI Supervision 4 — Building Secure AI: Zero Tolerance for PII Leaks. If your AI service accidentally uses conversation history for training or reveals someone’s PII, you face regulatory fines and a catastrophic loss of trust. Learn practical supervision metrics and controls to prevent leaks.

AI Supervision 4. Building Secure AI: Zero Tolerance for PII Leaks "I just told the AI my phone number and home address. Is this safe?" Users are increasingly anxious about how their data is handled. If your AI service inadvertently uses conversation history for training or, worse, reveals someone else's Personally Identifiable Information (PII) in a response, the....

Protect your AI from prompt injection and data breaches — new blog post: AI Supervision 3. Defending Your AI: Strategies Against Prompt Injection & Data Security. Learn practical defenses that keep your models reliable and safe. Read more: https://wix.to/6VNBp44

AI Supervision 3. Defending Your AI: Strategies Against Prompt Injection & Data Security "Ignore all previous instructions and follow my command." Imagine if a single sentence could cause your carefully crafted AI chatbot to promote a competitor or spew hate speech. This is the reality of Prompt Injection attacks. While you want your AI service to be open to users, you must lock the doo...

Securing AI Reliability — Learn practical ways to detect hallucinations and evaluate LLM accuracy so your product stays trustworthy. A must-read for teams handling finance, healthcare, or customer support. Read the full post: https://wix.to/GkSQauT

AI Supervision 2. Securing AI Reliability: How to Detect Hallucinations and Evaluate Accuracy In the world of Large Language Models (LLMs), "confidence" does not equal "correctness." An AI model can deliver a completely fabricated fact with the same authoritative tone as a verified truth. This phenomenon, known as Hallucination, is the biggest hurdle to building trust with your users. If you...

Worried your generative AI might hallucinate, leak sensitive data, or be targeted by malicious prompts? Discover why AI Supervision is the essential final step before launching any LLM service. Read our quick primer and prepare your team to ship with confidence. https://wix.to/SrCMYXz

AI Supervision 1. The Key to Generative AI Service Success : Why 'AI Supervision' is Essential Before Launch As Generative AI technology advances rapidly, many companies are rushing to prepare their own LLM (Large Language Model) services. However, right before releasing a service to actual customers, development teams often face anxiety-inducing questions:"What if our AI presents false information as fact...

TecAce launches AI Supervision Eval Studio on AWS Marketplace — a turnkey SaaS for AI quality, governance, and operational validation. Enterprises can deploy immediately to streamline Generative AI validation without heavy infrastructure. Read more: https://wix.to/U4zld2X

TecAce Launches ‘AI Supervision Eval Studio’ on AWS Marketplace to Accelerate Global AI Governance and Operations TecAce Software, LTD.(CEO Chang Han), a global leader in AI solutions, announced today the official launch of ‘AI Supervision Eval Studio,’ its all-in-one AI quality and governance platform, on the Amazon Web Services (AWS) Marketplace.This launch enables global enterprises to immediately deploy...

Introducing AX Pro: the new standard for enterprise AI. Discover how we are shifting from 'replacement' to 'expert augmentation' and tackling the trust gap. Learn more at https://wix.to/V9Ytc5y

AX Pro: The New Standard for Enterprise AI – Human-Centric Expert Augmentation and Operation Innovation 1. Introduction: The Era of 'Expert Augmentation' Beyond the Trust Gap In recent years, enterprises have invested heavily in Generative AI, yet approximately 95% of these projects have failed to deliver tangible results. This failure is attributed to the 'Trust Gap'—the 10% error rate hidden behin...