Understanding LLMs Utility versus Intelligence

From: aidotengineer

Travis Fry Singinger, Technical Director of AI ETHLite, presents a top-down understanding of Large Language Models (LLMs), specifically addressing “the coherency trap” – why prompting feels like magic but isn’t intelligence [00:00:00]. This perspective aims to explain why LLMs perform so effectively, even though they lack intent, desire, or true intelligence [00:00:20].

Initial Impressions and the GPT-4 Shift

In November 2022, the release of GPT-3.5 generated considerable hype, but Fry Singinger’s initial experience was disappointing [00:00:37]. While it showed advancements in tasks like improving emails, it was “very brittle in its understanding,” with surface-level fluency often collapsing at edge cases due to prompt sensitivity and context limits [00:01:02].

However, the release of GPT-4 in January 2023 marked an “uncanny moment” where words aligned effortlessly, creating a profound sense of understanding [00:01:22]. This shift was widely noticed; Microsoft Research published “Sparks of Artificial General Intelligence: Early Experiments with GPT-4,” and academics like Ethan Mollick began research into this new phenomenon [00:01:37]. This experience of perceived utility surpassed previous AI encounters, feeling as though the output genuinely understood the input [00:02:03]. Fry Singinger felt there was a significant space between “very dumb chatbots” and AGI, which he sought to explore [00:02:35].

Experimental Exploration of LLM Capabilities

To understand this new behavior, Fry Singinger, an engineer and scientist, began conducting experiments [00:02:43].

Paired Programming with LLMs (Vibe Coding)

He started by live-streaming his work with ChatGPT, engaging in what he termed “chat assisted programming” (also known as “chat oriented programming” or “vibe coding”) [00:03:18]. While initially challenging to produce usable code, these sessions evolved into prototypes for what AI pair programming would become [00:03:34].

A key utility developed during this phase was Webcat, a Python Azure function designed to scrape web pages for content [00:04:00]. This tool was crucial because early ChatGPT-4 models lacked internet access, making it difficult to chat about or explore ideas from current web content [00:04:16]. This demonstrated the utility of LLMs when augmented with external services.

Collaborative Content Creation

Further experiments involved building his blog, AIBuddy.software, with AI assistance [00:04:33]. The goal was to lean into the collaborative essence of AI [00:04:47]. The AI even helped select the platform (Ghost) and, using Webcat, pulled in article snippets to aid in content generation [00:04:59]. The success of this thought leadership blog demonstrated the AI’s capability in content creation [00:05:36].

AI-Assisted Creative Production

Fry Singinger then ventured into music production, aiming to create a concept album titled “Mr. Fluff’s Reign of Tiny Terror,” a feline metal album [00:06:00]. He used ChatGPT for lyrics and music composition, alongside image editing to maintain consistency across interactions [00:06:23]. This project highlighted new prospects for creative partnering with LLMs [00:06:43]. Despite the humorous and “handicapped” nature of the project (AI-generated cat metal), the YouTube videos garnered over 3,000 views and positive comments within a month, demonstrating the AI’s ability to help create valuable content beyond individual capabilities [00:07:01]. This indicated that LLMs could create a single, coherent concept across various modalities [00:07:35].

The AI Decision Loop: Nudge and Iterate Framework

To further understand reliable interactions, Fry Singinger explored elements of decision intelligence and pairing behavior, analyzing his ChatGPT history [00:07:45]. He built an analysis tool to extract qualitative and quantitative metrics around these behaviors [00:08:21]. This led to a 21-page research paper outlining the technique and prompts [00:08:50].

The outcome was the “AI Decision Loop,” later simplified into the “Nudge and Iterate” framework:

• Frame: Define the problem and context (prompt engineering) [00:09:35].
• Generate: Produce outputs, whether single or multiple [00:09:45].
• Judge: Evaluate the quality and fit of the output [00:09:57]. (Optionally: Validate against external requirements) [00:10:04].
• Iterate: Refine the prompt to improve the experience based on what was right or wrong [00:10:16].

This cycle, “Frame, Generate, Judge, Iterate,” proved crucial for achieving reliable outputs, nudging the model towards desired results [00:10:40].

Coherency Theory: The LLM Superpower

Despite understanding the mechanics, the question of why LLMs work so well without being intelligent remained [00:11:17]. Fry Singinger proposed “coherency theory,” inspired by natural language processing and research from Anthropic on feature superposition and concept circuits [00:11:27].

Coherence is defined as a system property, not a cognitive one [00:12:01]. It’s the underlying infrastructure through which thought navigates [00:12:06]. Its four key properties are:

1. Relevant: Output feels topical, connected, and purposeful [00:12:24].
2. Consistent: Maintains a singular tone, terminology, and structure across multiple interactions [00:12:33].
3. Stable: Can withstand pressure, questioning, or competing theories without collapsing; it may firm up or course-correct [00:12:51]. This stability was notably absent in earlier models like GPT-3.5 [00:13:15].
4. Emergent: A property that appears without explicit training. For example, GPT-4o was not trained to detect swine disease but can diagnose it through “coherent pattern alignment,” similar to certain cancer diagnoses [00:13:21].

The Mechanics of Coherence

Traditionally, neural networks were thought to store concepts in single neurons [00:13:53]. However, research suggests that LLMs use superposition, representing complex ideas with fewer parameters by packing more nuance into the same space [00:14:09]. As context accumulates, the network teases apart relevant meanings and collapses ambiguity into coherent output [00:14:20]. For instance, neurons might store elements of “feline,” “pet,” and “animal,” which can also overlap with “canine,” “pet,” and “animal” [00:14:31]. This means meaning is constructed on demand from distributed “sparks of possibility,” rather than being simply retrieved [00:14:52].

Prompts act as “force vectors” within the high-dimensional latent space of the AI model [00:14:59]. Each prompt sets a specific direction, causing the AI to align patterns [00:15:06]. When external context is provided, conceptual “clouds” (subnetworks) in the latent space activate. For example, “storytelling” and “pets” concepts light up and merge to create a new, coherent idea based on the specific interaction [00:15:53]. This demonstrates that LLMs recreate the essence of an idea and combine multiple essences to create something new, rather than just retaining compressed information [00:17:08]. This is why even hallucinations can feel correct; they are compelling pattern constructions, not fact-checking [00:17:20].

Engineering for Coherence, Not Intelligence

Framing systems as coherent rather than intelligent changes how we approach LLM engineering [00:17:35]:

• Hallucinations as Coherence Indicators: Hallucinations are a system feature, not a bug [00:17:45]. They indicate the model’s attempt to complete a pattern following its internal logic, especially when insufficient information is provided [00:17:52].
• RAG as Factual Anchors: Retrieval Augmented Generation (RAG) fragments act as “factual anchors,” providing contextual gravity that pulls the concept in the right direction [00:18:12]. They serve as structural scaffolding for coherence [00:18:27].

Fry Singinger proposes a three-layer model for LLM operation [00:18:41]:

• Layer 1: Latent Space: The internal model structure (concepts, weights, activations) [00:18:45].
• Layer 2: Execution Layer: Tools, APIs, and retrieval mechanisms that bring extra context for Layer 1 [00:18:51].
• Layer 3: Conversational Interface: Where human intent and thought are passed to the machine, grounding Layer 1 and 2 to make them actionable [00:19:00].

To build for coherence:

• Prompting as Interfaces: Prompts are components in a system, not one-off interactions [00:19:19].
• RAG for Grounding: Use dense, relevant context to steer generation, acting like gravity pulling output towards reality [00:19:26].
• Design for Emergence: Accept that LLMs are not deterministic; build around the “Frame, Generate, Judge, Iterate” loop [00:19:34].
• Avoid Fragile Chains: Long reasoning chains can break coherency. Keep chains modular and reinforce context at each point [00:19:42].
• Monitor Breakdowns: Watch for shifts in tone, structure, or flow. These are early signs of context loss, indicating a need for intervention or debugging, such as adjusting chunk sizes in a vector database or integrating other tools [00:19:53].

In conclusion, LLMs are not intelligent thinkers but “high-dimensional mirrors” that resonate through structure, not thought [00:20:17]. Their superpower is coherence, and the “magic” lies in the collaborative dance between human intent and the model’s structured resonance [00:20:34]. The focus should shift from chasing intelligence to designing for structured resonance [00:20:40].