Should Custom Software Teams Go All-In on LLMs? ChatGPT Itself Gave the Answer

Introduction: A Candid Conversation with AI

While everyone is talking about "going all-in on LLMs," a developer from a custom software team did something interesting — they directly interviewed ChatGPT itself, asking the large language model whether every scenario should integrate LLMs.

The answer was surprisingly rational: Don't use AI just for the sake of AI.

This conversation covered multiple dimensions including scenario selection, cost analysis, and industry fit. For ToB developers and custom software teams, it offers highly valuable reference points. Below are the core insights and in-depth analysis distilled from this conversation.

Fixed-Logic Scenarios: LLMs Are Not the Optimal Solution

The first core question explored in the conversation was: Do scenarios with fixed processing logic and determined data formats need LLM integration?

ChatGPT gave a clear answer — no. If the processing logic and data formats are both fixed, traditional rule-driven or scripting systems are often more efficient and controllable. For these fixed workflows, you should prioritize keeping existing solutions.

To understand this perspective, you need to first grasp the fundamental limitations of LLMs. Large Language Models are based on the Transformer architecture, predicting the next most likely Token through statistical learning from massive corpora. This probabilistic prediction mechanism makes them naturally good at handling semantically ambiguous, open-ended problems, but it also means their output is essentially "most likely correct" rather than "definitely correct." For deterministic tasks, this probabilistic nature is actually a flaw — you can't guarantee it will return perfectly consistent results every time like calling a function, which is an unacceptable risk in industrial-grade systems.

Consider a typical localized application scenario: using OCR scripts for screen monitoring, reading recognized data to determine whether values reach a threshold, then triggering voice alerts or sending notification messages. The judgment logic and trigger conditions in such scenarios are relatively simple, and local scripts are more than sufficient.

This insight reveals a fact many teams overlook: The core of technology selection isn't about "how new it is" but "how well it fits." LLMs excel at handling ambiguity, complexity, and open-ended problems, while for highly deterministic tasks, traditional solutions are superior in efficiency, cost, and controllability.

Personal Scenarios and Small Businesses: Local Code Is More Practical

The conversation further focused on personal work scenarios: when logic is fixed and data formats are determined, should you choose LLMs or local code?

ChatGPT's recommendation remained local code. The reasons are straightforward:

• Lower cost: No API call fees required
• Simpler logic: No need to handle prompt engineering, context management, or other additional complexity
• Stronger controllability: Deterministic output with no "hallucination" risk

It's worth explaining the "hallucination" problem — this is a professional term in the LLM field referring to models generating content that appears reasonable but is actually incorrect. Since LLMs are probability-based text prediction systems rather than true logical reasoning engines, when encountering problems insufficiently covered by training data, they tend to "fabricate" a plausible-sounding answer rather than admitting they don't know. For business scenarios requiring precise output, this characteristic is fatal. Meanwhile, Prompt Engineering — carefully designing input prompts to guide model output — itself requires additional technical expertise and ongoing debugging costs, further raising the barrier to LLM adoption.

LLMs often introduce unnecessary complexity and additional costs in personal scenarios. This is especially important for small teams considering "should we add an AI feature to our product" — not every feature needs AI enhancement; users want their problems solved, not solutions that look cool.

Which Industries Truly Need LLM Integration?

Of course, LLMs are not without their place. ChatGPT identified several industry directions that genuinely need LLM capabilities.

Industry Characteristics Suited for LLMs

• Finance: Risk assessment and dynamic decision-making requiring massive data support
• Healthcare: Complex diagnostic assistance and pattern recognition
• Manufacturing: Anomaly detection and data analysis (note: not real-time control)
• Supply Chain Management: Complex conditional judgment and dynamic optimization

These industries share common characteristics: decisions require massive data support, dynamic pattern recognition, and complex conditional judgment. LLMs can significantly improve efficiency and decision accuracy in these scenarios. More precisely, the core characteristic of these scenarios is the presence of large amounts of unstructured information that needs to be understood — natural language descriptions in medical records, semantic analysis in financial reports, multi-variable dynamic interactions in supply chains. This is precisely where the cross-domain knowledge graph formed through LLM training on massive corpora can deliver value.

Key Distinction: Analytical Assistance vs. Real-Time Control

The conversation specifically discussed scenarios for production enterprises. When working with PLCs for production process management, LLMs can serve as auxiliary tools — helping analyze complex data, generate reports, and identify anomalous patterns, but must never directly replace PLC real-time control.

It's necessary to explain the core position of PLCs (Programmable Logic Controllers) in industrial automation. PLCs are embedded computers designed specifically for industrial environments, using deterministic operating systems (RTOS) capable of executing control commands with millisecond or even microsecond response times, operating stably under extreme temperatures, vibration, electromagnetic interference, and other harsh conditions. The core design philosophy is "determinism" — identical inputs must produce identical outputs, and responses must be completed within specified time windows. In contrast, LLM API calls typically require hundreds of milliseconds to several seconds of latency, with response times unstable due to network conditions, server load, and other factors. On a production line, even a single response timeout could lead to equipment damage or safety incidents — this is a fundamental architectural conflict. Therefore, the correct positioning of LLMs in industrial scenarios is the offline analysis layer: processing historical data, generating optimization recommendations, and assisting human decision-making, while real-time control authority at the execution layer must remain with PLCs.

Core real-time control still needs to rely on dedicated industrial automation systems. This distinction is crucial: LLMs excel at "thinking," not "executing." In industrial scenarios, even millisecond-level response delays can cause serious consequences — that's not a responsibility LLMs can bear.

Token Cost Trends: Unit Price Drops but Total Spending Rises

The conversation also touched on a topic developers care deeply about — Token cost trends.

ChatGPT cited industry trend data: Token costs for economy-tier models decrease by approximately half each year, with leading-tier models also gradually declining. This is good news.

To understand the practical significance of this trend, you need to first understand how Tokens are measured. A Token is the basic unit for LLM processing and billing, corresponding to approximately 0.75 English words or 0.5 Chinese characters. The cost of each API call consists of input Tokens (content you send, including system prompts, conversation history, and user input) and output Tokens (content generated by the model). A business system Prompt with detailed background descriptions can consume thousands of Tokens just for the system prompt portion alone, and with accumulated context from multi-turn conversations, single-call costs can be significantly higher than simple Q&A scenarios. Looking at industry data, API prices for GPT-4-level models have indeed experienced significant drops since 2023, primarily driven by inference efficiency optimization, hardware cost reduction, and intensifying market competition.

But there's an easily overlooked trap here: Although unit costs are declining, overall consumption will rise due to increased usage volume. As LLMs are called in more workflow stages, the Token volume consumed per interaction also increases (longer contexts, more complex prompts), meaning enterprises' total AI spending may actually rise rather than fall.

This means enterprises need more granular Token consumption management:

1. Precisely define which stages truly need LLM intervention
2. Optimize prompt design to reduce unnecessary Token waste
3. Establish monitoring mechanisms to track Token consumption per business module
4. Adopt hybrid architectures — use rule engines for simple tasks, call LLMs only for complex ones

A Practical Decision Framework for ToB Developers

Synthesizing the core insights from this conversation, we can summarize a clear technology selection decision framework for custom software teams.

Scenarios Where Local Solutions Should Be Prioritized

• Fixed processing logic with determined data formats
• Local data processing, LAN-based operations
• Automation scripts simulating manual operations
• Real-time control in coordination with PLCs and other hardware
• Lightweight needs for individuals or small teams

Scenarios Where LLM Integration Should Be Considered

• Natural language understanding and generation required
• Complex pattern recognition and dynamic decision-making involved
• Data analysis and report generation
• Unstructured data processing
• Business logic requiring continuous learning and adaptation

In practice, Hybrid Architecture is becoming the mainstream approach in the ToB space: using rule engines, state machines, or traditional algorithms for highly deterministic business processes, and calling LLM APIs only at nodes requiring semantic understanding, content generation, or complex reasoning. This architecture not only significantly reduces Token consumption but also lowers hallucination risk by narrowing the LLM's decision boundary, while preserving overall system predictability.

Core principle: Technology serves the business, not the other way around. Staying calm amid the LLM hype and making rational choices based on actual scenarios is the truly responsible attitude toward clients and teams.

Conclusion

The most valuable aspect of this conversation is that even the LLM itself is saying "don't blindly use LLMs." This isn't modesty — it's technical rationality. For ToB custom software teams, true competitiveness doesn't lie in how much AI you use, but in whether you can choose the most appropriate technical solution for your clients. Amid the noise of "all-in on LLMs," maintaining clear judgment may be the scarcest capability of all.