LATEST TECH ARTICLES

"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 that Retrieves, Generates, and Evaluates. In this final article of our series, we present a practical blueprint for integrating AI Supervision  into your RAG (Retrieval-Augmented Ge

"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 Slack, or complex backend workflows. The gap between the testing environment and the production environment often leads to unexpected bugs. AI Supervision  bridges this gap with r

"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 "Average Score" hides the critical details necessary for these decisions. Use AI Supervision 's Detailed Analysis & Comparison features  to put your model under a microscope and see

"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-quality model will fail if it's too expensive to run or too sluggish for the user. Here is how you can use AI Supervision 's Real-time Insights Dashboard  to visualize and optimiz

"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 evaluation results cannot be trusted. It’s time to ditch the inefficient file-based workflow. Build a centralized TestSet Management System  with AI Supervision . Systematic Test

"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 also prone to human bias, often missing critical edge cases. It's time to liberate yourself from manual data entry with AI Supervision 's TC Generator . 1. What is TC Generator ?

"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 consequences are severe. This isn't just a bug; it's a security breach that can lead to massive legal penalties (like GDPR fines) and a total loss of trust. AI Supervison Metrics

"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 door against bad actors. In this article, we explore the dangers of prompt injection and how AI Supervision  provides an ironclad defense strategy. 1. Prompt Injection: Hacking wit

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 your service provides financial advice, medical information, or customer support, a single hallucination can lead to reputational damage or critical errors. So, how do we move from

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?" "What if a user attacks the system with malicious questions?" "Is there a risk of sensitive personal information being leaked?" The solution that resolves these concerns and

AI Supervision Eval Studio 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 AI Supervision as a SaaS solution, streamlining the complex process of validating and operating Generative AI applications without heavy infrastructur

As of 2025, most companies are adopting their own AI systems, but how many are systematically verifying their safety? According to a recent report by MIT Technology Review, 54% of companies still rely on manual evaluation methods, and only 26% have started automated assessments. This is clearly insufficient compared to the growing threats to AI security. What Is an AI Red Team? An AI Red Team extends the traditional cybersecurity red team concept to AI systems. According to d

1. Introduction Recently, the use of AI Agents has been increasing across various industries. Moving beyond simple chatbots that answer questions, they are evolving into forms that can autonomously assess situations, utilize necessary tools, and derive results. However, for these agents to operate as safely and accurately as expected, systematic evaluation, verification, and continuous monitoring are essential.This article introduces the necessary evaluation, verification, an

TecAce Software is thrilled to announce its participation in the SK AI Summit 2024. This prestigious event provides an excellent platform for TecAce to present its innovative AI Supervision solution, designed to help businesses effectively control and monitor AI applications, LLM models, and data. This solution ensures stable and reliable AI utilization, enhancing industry stability and mitigating potential threats. Event Details: 2024. 11. 04 – 05 | COEX SEOUL, KOREA Exhibit

In recent years, artificial intelligence (AI) has emerged as a transformative force, reshaping industries, businesses, and even our daily lives. But what if I told you that understanding AI requires us to step away from the conventional digital lens we’ve relied on for the past few decades? Instead, AI aligns more closely with the analog world—closer to human cognition than to machine computation. In this article, I aim to explore why AI’s true potential lies beyond the binar

Recent Advances in AI and the Emergence of AI Governance with the recent rapid advancements in artificial intelligence (AI) technology, its influence is deeply permeating our daily lives. AI is enhancing human efficiency and convenience in almost every field, including healthcare, education, finance, and manufacturing, creating new value. However, these technological innovations are intertwined with both opportunities and challenges. Behind the convenience and possibilities o

The rapid advancement of Large Language Models (LLMs) has revolutionized the field of artificial intelligence, pushing the boundaries of what machines can understand and generate. Models like GPT-4 and beyond exhibit capabilities that were once thought to be years away. However, this swift progress has highlighted significant limitations in traditional benchmarking methods, prompting a reevaluation of how we assess these sophisticated models. In this article, we'll explore wh

Introduction Artificial intelligence, particularly generative AI, is at the forefront of technological innovation. While many companies are attempting to adopt this revolutionary technology, the journey appears to be more challenging than anticipated. According to a recent Forbes report , approximately 90% of Generative AI Proof of Concept (PoC) projects fail to reach the actual production stage. This suggests a significant gap between the potential of AI technology and its p