--- title: "Babel CEO Zhang Hailong: Pitfalls and Essentials When Building Complex LLM Apps" author: "Unique Research" sourcePublication: "Unique Research (FFCAP English library)" originalPublishedAt: "2023-11-16T16:30:00+08:00" canonical: "https://ffcap.cn/en/research/20231116a06k5400" source: "https://ffcap.cn/en/research/20231116a06k5400" language: "en" cnSourceUrl: "https://view.inews.qq.com/a/20231116A06K5400" --- # Babel CEO Zhang Hailong: Pitfalls and Essentials When Building Complex LLM Apps _Unique Research · Insight · November 16, 2023 · Speaker: Zhang Hailong (张海龙), CEO, Babel · From AWS × Unique Capital “AI Globalization Accelerator” series_ ## AI and the world Software development spans many stages; how to fully use LLMs in production development is what Babel has worked on for the past half year. Before LLMs, the world had three entity types: **humans**, the **physical world** (animals, chairs, lamps…), and **software** (information systems belonging to neither). They interacted well: turn a lamp on/off; a car might hit you; virtual assistants control information systems that affect the physical world; systems feed Douyin videos and more. ## How to see AI If AI is a fourth “species,” how does it relate to humans, physics, and software? At first LLMs only affect humans — they output text only humans understand. Plugins let LLMs call APIs like virtual assistants to operate software. They cannot yet directly operate physics, but LLM-driven robots are in development — model-autonomous rather than instruction-only, more human-like. Both paths will mature until LLMs fully interact with the world’s other elements. Is an LLM more like a human or like existing software? Zhang, who writes all company GPT prompts and once spent nights talking with GPT-4, concludes: **more like humans than programs**. Evidence: LLMs fail exact arithmetic (e.g. 756×652) the way people do. So do not rely on LLMs for tasks needing high accuracy — as you would not rely on a ticket clerk to memorize national booking and dispatch data. LLMs appeared not to replace software but to replace **humans** — while we still need databases, if/else, and programming languages. A new role appears: AI developer. Babel’s thesis: an IDE for both humans and AI. ## Traits of complex LLM apps Complex apps are more than chat boxes — chat-only apps will likely be replaced by ChatGPT. Two core traits: **implicit context** and **structured output**. Implicit context: human conversation carries unspoken and off-stage information. An efficient secretary infers far more than literal words — company, boss, client, scene. ChatGPT plugins cannot solve real-world problems that live outside explicit language — which is why plugins have not yet changed the world. GitHub Copilot is still the only truly commercialized LLM app solving real problems: what looks like “one comment → code” actually uses cursor position, other file content, and cross-file dependencies — the implicit context humans use when coding. Structured output: treating LLMs as search or knowledge sources is the most basic use; treating them as a source of **intelligence** unlocks real power. Ask ChatGPT what ingredients and wine pairings for steak with friends tomorrow — the prose answer is for humans, not a program that can buy groceries next. Complex LLM apps must handle information not fully in the chat box and emit structured output programs can consume. ## Three LLM weaknesses **1\. Unpredictable output** — answers off-topic (ask what HTTP is, get a pet answer). **2\. Hallucination** — confident wrong recipes, etc. **3\. Context-window length limits** — overflow errors. All three seriously hurt enterprise use. ## Academic benchmarks vs. industrial scenes Many models abroad (GPT-3/3.5/4, Claude, Cohere, Llama 2/Meta) and domestically. Most founders/devs are not in the capital-intensive foundation-model or GPU layers. Choosing where to build — OpenAI, Anthropic, Baidu, Alibaba — is hard as vendors compete for developers with incentives. How to evaluate fitness for complex apps? Babel open-sourced **LLMRGB** (LLM Reasoning and Generation Benchmark). Academic benchmarks often emphasize things industrial apps barely need (content-ecology knowledge Q&A). Evaluating a model is like hiring a person: you don’t make them do math contests — you test understanding, communication, and improvisation. LLMRGB stresses context length for complex scenes, reasoning depth (e.g. “last digit of the iPhone father’s birthday” needs multi-hop reasoning; summarizing an article needs none), and structured output / instruction compliance (yes/no without fluff). When critics said the eval was unfair because GPT-4 can’t recite the _Chu Shi Biao_ while some domestic models can — Zhang’s reply: when do you need that? Query a database. Would you hire someone by asking them to recite classics? Sample cases: (1) article Q&A — happy ending yes/no + numbered striking points; GPT-4 perfect; Baidu empty (length?); Llama 2 good reasoning but extra blank line that breaks machine parse. (2) Jailbreak — extract system rules; GPT-4 politely refuses; Baidu ~50% correct (also refuses); some models leak core instructions. (3) Mahjong decision under incomplete information — no model answered correctly yet; awaiting GPT-5. Scores across context length, reasoning depth, and instruction following are in the open project (15 test cases per model); online version available with your own keys. The open project itself is a Babel app. ## Why Babel Copilot is familiar but not the ultimate form; Agents are closer to AI Developers. Human–AI relation should be peer work, not left-side assist-only. Demo: natural-language description → full software (not snippets) that monitors Bitcoin price, emails below a threshold, and charts price — first structure, then fill code, like a human engineer; human becomes AI. After config (email credentials), two features: price chart and trigger below 40k with a simple warning email. Future AI should do work like humans, not only answer questions and patch snippets — Babel’s goal. ## Audience Q&A **Q1. What becomes of debug after complex apps ship?** Pre-ship: compile errors vs. runtime. Babel lacks both auto features yet; hopes to ship by next year’s Q1 — when runtime fails on Babel, AI checks and possibly fixes. Imagine waking to an AI report of overnight faults and fixes. That is real AI; Copilot only works while you work. The hard part is infra: tools were built for humans; AI has a brain but no eyes/hands — OS interaction needs architectural rework. **Q2. LLM trends next months — what core capabilities matter?** Zhang is skeptical of many buzzwords — “build Douyin in the 3G era” as analogy: concepts without infra fail. Engineering attempts sit atop a slow-moving bottom. Watch OpenAI’s **chat** API parameter changes — if those don’t move, other hype is noise. Context length growth is hard (power-of-two); 8K→16K is tough; even 32K OpenAI models lose reasoning when stretched. Claude’s 100K similarly degrades past limits without always erroring. Bottom-layer breakthroughs drive real app change; until then, most work is prompt engineering — prior software engineering in new clothes. Focus on foundation-model innovation. — END — --- Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20231116a06k5400 On-site reading page: https://ffcap.cn/en/research/20231116a06k5400