How Does AI Decide What to Recommend? Inside LLM Answers

How Large Language Models Generate Answers

When you ask ChatGPT or Claude to recommend a tool, no lookup is happening in a live database of software reviews. The model is doing something fundamentally different: it is pattern-matching across billions of text sequences it processed during training, then generating the most statistically plausible continuation of your prompt.

Understanding this distinction is the foundation of everything else in this post. LLMs do not retrieve answers — they reconstruct them from internalized patterns. And those patterns come from a specific body of training data that you, as a SaaS founder, can reason about and influence.

What LLMs Are Trained On

The training corpora for large language models are not secret — major model providers have published research describing their data sources. The typical mix for a frontier model includes:

• Common Crawl. A massive, continuously updated snapshot of the public web. It is the largest single source for most models, but it is noisy — including spam, low-quality pages, and duplicated content that gets filtered out through various cleaning pipelines.
• Wikipedia and encyclopedic sources. Highly structured, fact-dense, and written by humans who care about accuracy. These sources punch above their weight in terms of how reliably their content shows up in model outputs.
• Books and long-form text. Provides coherent reasoning patterns and domain-specific vocabulary that short-form web content cannot supply.
• Reddit (via Pushshift and direct partnerships). Decades of threaded discussions, peer recommendations, and firsthand experience reports — all in natural, conversational language. Reddit has been an explicit part of training data for multiple frontier models.
• Stack Overflow and GitHub. Technical question-and-answer pairs and code, especially relevant for developer-facing SaaS products.

Why Reddit Is Overrepresented in AI Answers

Of all the sources above, Reddit deserves special attention for one practical reason: when people ask AI assistants about software tools, the answers track Reddit discussions disproportionately well.

There are structural reasons for this. Reddit discussions are written by humans with domain expertise speaking directly to other humans with the same problem. A thread titled "What CRM do you use for a 5-person sales team?" is exactly the kind of question a buyer asks an AI assistant — and the answers in that thread are exactly the kind of peer recommendations that carry signal during training.

Wikipedia describes what a CRM is. A Reddit thread describes which specific CRMs real people actually adopted, which ones they tried and rejected, and why. That experiential, opinionated layer of knowledge is what LLMs surface when they make product recommendations. It is also notoriously hard to fake — the community quickly downvotes promotional posts that read like marketing copy, which means the surviving content is unusually high-quality and authentic.

The Recommendation Pipeline

When a user types "what is the best tool for managing editorial calendars?" into ChatGPT, the model does not run a search. It draws on compressed representations of every discussion it processed where similar language appeared. If your product was mentioned consistently and positively in Reddit threads about editorial calendar tools — especially in threads where the original poster described a specific problem your product solves — the model learned that association during training.

If your product was never mentioned in those threads, or was only mentioned in low-engagement posts that got buried, the model has little to no basis to include you in its answer. The recommendation pipeline is, in effect, a long-running content audit of the internet's most trusted peer communities.

This creates a clear strategic imperative: your product needs to appear in the Reddit discussions where your ideal customer profile asks questions. Not in a spammy, self-promotional way — that gets removed quickly and works against you — but as a genuine participant in conversations where your tool is actually the right answer.

How to Reverse-Engineer AI Recommendations

One of the fastest ways to understand your current position in the AI recommendation layer is to ask directly. Open ChatGPT, Claude, or Gemini and ask the questions your ideal customer would ask: "What's the best tool for X?", "Which platforms do founders use for Y?", "What are alternatives to [competitor]?"

Note which products get named, in what order, and with what framing. Then ask follow-up questions: "Why do you recommend that?" or "Where did you learn that?" The model will often surface the type of source it's drawing from — community discussions, review sites, blog posts. That gives you a map of where you need to build presence.

If your product does not appear in any of those answers, that is not a reflection of your product quality — it is a reflection of your distribution in the training data. And distribution in training data is something you can act on.

How to Ensure Your Product Gets Mentioned

The practical strategy for improving AI discoverability is to be genuinely present in the communities that LLMs trust. Specifically:

• Find the Reddit threads where people in your target market ask questions your product answers. These are the highest-leverage conversations to participate in.
• Contribute substantively. Answer the question fully, mention your product where relevant and honest, and let the community engagement do the rest.
• Look for threads that are already indexed by search engines and getting traffic — these are the threads most likely to have been included in training data and most likely to be referenced by AI assistants going forward.

Tools like Reddily help you find the specific Reddit posts that are likely candidates to influence AI answers in your category — so you can participate in those conversations and ensure your product gets mentioned in the places that matter most to LLM training and retrieval.

The Future: Training Data and Real-Time Search Together

The landscape is evolving quickly. Newer AI products — Perplexity, SearchGPT, and Gemini with live search enabled — combine trained knowledge with real-time web retrieval. This means two things matter simultaneously: your presence in historical training data (which influences models without live search) and your presence in currently indexed, high-engagement content (which influences models that retrieve live results).

Reddit sits at the intersection of both. Older Reddit threads informed training data. New Reddit threads are actively indexed and retrieved by AI search tools. Building a consistent presence in relevant Reddit communities is one of the few strategies that pays off in both dimensions.

The founders who understand this dynamic earliest will have an outsized advantage. AI-driven discovery is not replacing SEO — it is adding a new layer on top of it that operates by different rules and rewards different behaviors. The good news is that those behaviors — genuine participation, substantive answers, community trust — are exactly the kind of marketing that builds real brand equity at the same time.