The AI Answer Stack

When you order a dish at a restaurant, you don't think much about where the ingredients came from. You just evaluate what lands on your plate. But the chef is drawing from multiple sources, each with different trade-offs in cost, availability, and quality.

Some ingredients are generic: salt, flour, chicken stock. These can be purchased in bulk from any restaurant supplier or, if you’re in a pinch, a local convenience store. It would be impractical for a restaurant to hunt down the world's greatest artisan flour when a good-enough option is available cheaply and conveniently. Other ingredients sit in the middle: the meat might come from a specialty restaurant supplier, the tomatoes from a local farmer's market. These cost more and take more effort to source, but they noticeably improve the final product. And then there are the ingredients that define the dish. Maybe it's an olive oil sourced from a small farm in Sicily—rare, expensive, difficult to acquire, but essential to what makes this restaurant's food worth seeking out.

Every restaurant makes these trade-offs. The goal isn't to optimize every ingredient. It's to assemble the right combination that delivers value while remaining practical, efficient, and profitable. A chef who insists on high-end sourcing for commodity ingredients will go out of business. A chef who uses commodity ingredients for everything will produce forgettable food.

AI works the same way. When you ask a question, the system isn't pulling from a single pantry. It's assembling a response from one of many sources—some generic and fast, some specialized and expensive, some rare and resource-intensive. Each layer has different trade-offs in cost, latency, and accuracy. Understanding which layers produced your answer explains why the same tool can feel brilliant one moment and useless the next.

Sometimes the AI makes these sourcing decisions for you, choosing which layers to invoke based on how it interprets your question. Other times, you need to push it. A good chef knows when generic olive oil isn't enough—they need to press their buyers to find exactly the right product, even if it takes longer and costs more. Marketers need to operate with the same mindset. As we leverage AI in our daily work and build tools that operate with AI logic, it's essential to understand the different layers of the stack and know when and how to dig deeper.

‍Layer 1: Base Model Knowledge

At the foundation of any AI response is the base model's own knowledge—everything the system learned during training. For ChatGPT, this means a vast amount of general information, facts, and language patterns absorbed from internet text up to a knowledge cutoff date. When you ask a straightforward question that falls within what the model learned, it can usually answer directly from memory.

This layer is fast. Ask "Who wrote Pride and Prejudice?" and the answer comes instantly because the information was already encoded in the model's weights during training. There's no searching, no retrieval, no computation beyond generating the response. The model just knows.

But base model knowledge has clear limitations. It's static after training. A newer model might know much more than an older one, but neither knows what happened last night. If you ask for the score of yesterday's Knicks game, no base model can answer correctly from this layer alone. Any system that does answer correctly must be invoking a deeper layer of the stack.

This is also why the same question can yield different answers from different AI tools. There are two potential reasons. The first is that a model trained more recently, or on a larger corpus, will have different base knowledge than an older or smaller model. The "intelligence" you're experiencing isn't just about reasoning capability, but instead, it's about what information got baked in during training. The second reason is that different models might interpret your prompt differently and automatically go deeper in the stack. One tool might answer from base knowledge alone while another decides your question warrants a web search or retrieval from a knowledge base. This isn't always visible to the user. You asked the same question, but you unknowingly triggered different layers of the stack—and got different answers as a result.

Layer 2: Prompt Context

The next layer is everything you feed the AI in the conversation: your question, the conversation history, any uploaded files, and system instructions. This is the AI's working memory for the current interaction.

In the early days of ChatGPT, prompt engineering mattered enormously because this was the only lever users had. The system couldn't fetch new information or search the web. If you wanted better answers, you had to frame your questions more carefully or provide more context upfront. The entire output quality depended on how well you could compress your intent into the prompt.

When I was in college, I had a reputation amongst my roommates for being good at “Googling things.” This was prompt engineering in 2010; but eventually, Google became much easier to use (or rather, Google Search became better at providing quality answers with less context), and so my ability to craft the perfect query became less valuable.

Today, prompt skill still matters, but it's no longer the sole determinant of outcome quality. Still, understanding how this layer works explains a lot of AI behavior that feels mysterious.

First, the model doesn't actually "remember" your conversation. It re-reads the entire context window every time it generates a response. This means recent information is weighted more heavily, creating a recency bias within long threads. It also means the model infers your preferences from the conversation history. If you seemed satisfied with a certain tone, depth, or approach in earlier exchanges, it will often over-index on that pattern going forward.

This is why long conversations drift. You might start a thread focused on margin protection, but after several exchanges where you seemed pleased with growth-oriented suggestions, the model will keep framing everything through a growth lens even when you want something different. The fix is often to start a new conversation and re-anchor your objectives explicitly.

Context windows are also finite. There's a limit to how much text the model can process at once. In long conversations, early context gets pushed out or summarized, which means the model might "forget" things you discussed at the beginning of a thread. This isn't a bug—it's a constraint of how these systems are architected.

Layer 3: Reasoning

As we covered in the previous chapter, reasoning refers to the model allocating extra compute to think through a problem before answering. This is the most misunderstood layer of the stack.

Reasoning here doesn't mean conscious thought or symbolic logic in the human sense. It means the system is decomposing tasks, holding intermediate representations, and evaluating alternatives before generating text. When triggered, the model essentially works through the problem step by step internally, breaking it into pieces before committing to a response.

This isn't automatic. Systems use heuristics to decide when reasoning is worth the cost: problem complexity signals (math, logic, planning), ambiguity in the question, stakes implied by certain words ("decide," "compare," "optimize"), or explicit instructions like "think step by step." The model, or the orchestration layer around it, asks: "Is a fast, fluent response sufficient, or is additional deliberation worth the cost?"

The key insight is that reasoning is expensive. It takes more time and more compute. So systems avoid it unless they believe it will materially improve the answer. This explains a frustrating pattern: obvious questions get shallow answers, but hard questions sometimes get under-thought answers too. The system's heuristics for detecting complexity aren't perfect. If your question looks simple but is actually tricky, the model might not engage its reasoning capabilities. Explicitly asking for step-by-step thinking still helps.

OpenAI's reasoning models (starting with the o-series) take this further. They're designed to produce a hidden reasoning trace—essentially a long, detailed internal explanation—before generating the final answer. This dramatically improves performance on tasks like math and coding, but it's slower and more expensive to run. Neither approach is "smarter" in an absolute sense. They're different tools for different problems.

Layer 4: Retrieval (RAG)

Retrieval Augmented Generation is where the AI pulls in information from outside sources to enhance its answer. This is the layer that makes AI useful for questions about your specific business, your documents, or information that wasn't in the training data.

Here's how it actually works. When you ask a question, the system converts your query into an embedding—a numerical representation of meaning. Then it searches a defined corpus (your uploaded documents, a company knowledge base, a project folder) for chunks of text that are semantically similar to your question. The most relevant chunks get injected into the prompt context, and the model generates its answer using both its own knowledge and the supplied text.

This is important: the model itself has no idea how those chunks were chosen. It just treats them as authoritative context. The model isn't "checking facts" or "deciding what's true." It's working within supplied evidence.

RAG doesn't make the model smarter. It narrows the world the model is allowed to speak about. This is why RAG feels powerful—and why it sometimes fails silently. If the wrong document chunk is retrieved, the model will confidently answer based on the wrong information. The model didn't reason incorrectly. It was given the wrong memory.

In ChatGPT, this layer shows up in Projects (where you can upload files and set project-specific instructions) and in Custom GPTs with knowledge retrieval enabled. Claude has a similar feature called Projects with knowledge bases. NotebookLM is built almost entirely around this concept—you upload sources and query across them.

The key characteristics of RAG: it's a closed world with known scope, high signal, and no freshness beyond the corpus. The failure mode is missing context. If the answer isn't in your documents, the model might guess or admit it doesn't know, depending on how it's configured.

Layer 5: Live Web Search

Live web search is where the AI goes out to the internet in real time. This layer trades recency for noise.

When you ask about current events, recent news, or anything that changes frequently, a model with web search capability will actually query the internet, retrieve relevant pages, and incorporate that information into its response. You might notice a slight delay while it searches, and the answer will often include citations or links to sources.

This is crucial for any query where the base model's knowledge would be outdated. But it introduces new problems. Search results are shaped by SEO (which is now evolving into AEO, or Answer Engine Optimization) and marketing-driven narratives. The AI is now working with information that might be optimized for ranking rather than accuracy. This is why web-backed answers often hedge more, cite more, and feel less confident. The system is dealing with conflicting claims and uncertain provenance.

The difference between RAG and web search is mostly about scope and control. RAG pulls from a pre-existing library of documents you've curated or uploaded—known sources with presumably known quality. Web search pulls from the open internet in real time, which means broader coverage but much more noise.

When does the system choose one over the other? Generally: if the question is about your information (internal data, historical decisions, stable reference material), RAG is the right layer. If the question is about the world (current events, market changes, competitor activity), web search is the right layer. If the system guesses wrong, the answer can feel plausible but off.

Understanding where live search fits in the stack matters for marketers thinking about AEO strategy. Many marketers, including our team at AdVenture, are now producing content aimed at shaping answer engine responses. But in the short term, the only way your content gets referenced or cited by an AI—whether that's ChatGPT, a Google AI Overview, or another tool—is through this live search layer. The AI is querying the web, finding your content (or not), and deciding whether to include it in its response. Traditional SEO principles matter here because they increase your likelihood of being retrieved when the model pulls from live search.

But that's not the full scope of AEO strategy in the long run. Eventually, these models update their base knowledge. New training runs ingest new information. Content that exists on the web today might become part of a model's foundational knowledge tomorrow—not retrieved in real time, but baked into the weights during training. When that happens, the rules change. The tactics that help your content rank in live search aren't necessarily the same tactics that help your content get absorbed into base model knowledge during a training run.

This is another reason why understanding the layers of the stack matters. If you're optimizing content for AEO without knowing which layer you're targeting, you're flying blind. Live search optimization and base knowledge optimization are different games with different rules—and conflating them will lead to misallocated effort.

Layer 6: Deep Research

Deep research is the top of the stack. This is where the AI conducts multi-step investigations across many sources, cross-verifies information, and produces comprehensive reports rather than quick answers.

This layer is fundamentally different from the others. It's not just "better search." It's a process. Deep research systems run multiple searches, compare sources, resolve contradictions, track provenance, and structure arguments. The output might include sections, citations, and even a summary of the AI's own reasoning process.

Think of it as assigning a task to a research analyst for an hour rather than asking someone a question. The system plans a strategy, breaks the query into sub-questions, performs a sequence of searches, reads various articles, and keeps track of what information each source provides. It doesn't stop at the first answer. It digs until it has a complete picture.

ChatGPT's Deep Research feature works this way. OpenAI describes it as "conducting multi-step research on the internet for complex tasks, finding, analyzing, and synthesizing hundreds of online sources to create a comprehensive report." The output is slower (potentially several minutes), drier, and more cautious—but much harder to poke holes in.

Deep research is overkill for most questions. That's why systems avoid it by default. You typically have to explicitly invoke this mode. But for open-ended analytical questions, competitive intelligence, or anything where thoroughness matters more than speed, this is where the richest answers come from.

The Trade-offs

Every layer of the stack involves trade-offs. There's no single "correct" depth for an answer.

Base model knowledge gives you speed and fluency but loses freshness and grounding. Prompt context gives you control but introduces fragility (long threads drift, context windows fill up). Reasoning improves correctness but costs time and compute. RAG provides accuracy within your corpus but can miss context that isn't there. Web search provides recency but introduces noise. Deep research provides robustness but takes time and expense.

Modern systems try to auto-select the right depth. They're getting better at it. But they'll never be perfect, which means users who understand the stack can get better results by knowing when to push for more depth or when to reset and start fresh.

Why This Matters for Marketers

The immediate application is understanding why your AI tools behave inconsistently. When an answer feels off, the question isn't usually "is the AI broken?" The question is "which layer of the stack produced this answer, and was that the right layer for this question?"

If you asked about something recent and got an outdated response, the system probably relied on base knowledge when it should have searched. If you asked about your own data and got a generic answer, RAG might not have retrieved the right documents. If you asked a complex question and got a shallow response, the reasoning layer might not have engaged.

But the deeper application is for marketers building AI-powered tools or evaluating AI vendors. When you're designing a system—whether it's a customer service bot, a research assistant, or an internal knowledge tool—you need to decide which layers of the stack to enable and when.

A customer service bot probably needs RAG (to answer questions from your documentation) but might not need web search (which could introduce noise or off-brand information). A competitive intelligence tool probably needs web search and possibly deep research, but RAG matters less unless you're comparing external findings to internal data. A simple FAQ assistant might only need base model knowledge plus prompt context if the questions are predictable and the answers are stable.

Each layer you add increases capability but also increases cost, latency, and potential failure modes. The art is matching the stack to the task.

Understanding this also protects you from vendor hype. When someone pitches you an "AI-powered" solution, you can ask: which layers of the AI answer stack does this actually use? Is it just a thin wrapper around a base model? Does it have access to your data through RAG? Can it search the web? Does it have any reasoning capabilities beyond standard pattern matching?

The answers to those questions tell you far more about what the tool can actually do than any marketing language about "advanced AI" or "intelligent automation."