What does this GEO / AEO and LLM Optimization all mean?
The optimization (improvement) of your content (be it website, video, images, document etc..) so that it has higher visibility on large-language models (LLMs) / GPT
GEO acronyms
acronyms
Meaning
GEO
Generative Engine Optimization
AEO
Answer Engine Optimisation
LLM
Large Language Models
This table explains each acronym used
In essence, similar to SEO (Search Engine Optimization). You want LLMs / GPTs to be able to:
Discover your content
Crawl and be able to extract information from this
Add this information to their knowledge graph
Answer questions in reference to this content
Common questions about Generative Engine Optimization (GEO):
Why don’t we just call it SEO?
Most people assume that “search” is in relation to a specific search engine such as Google, Bing, Duck Duck Go and so on. A GPT doesn’t “search” for spesific keywords / queries within an index of information. What it does is try to understand the intent of the prompt and deliver knowledge based on this.
One think you REALLY need to understand about modern LLMs:
Modern GPT LLMs often break prompts into search queries and run searches to find the most up to date information. They often only look on the 1st SERP (Search Engine Result Page) and then refresh the content they have. In essence, if the topic has some form of freshness i.e. younger than the last update of the model (GPT 4.0 is over 1 year old) then they will perform a search. Essentially, if you are not ranking on the first page of a search engine you will not be in the results.
Does this mean SEO is not dead?
100%, without search all models would be stuck to old and often out-data data. This is the only way the models currently can still give relevant information.
No, large language models (LLMs) do not explicitly use E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) to evaluate or rank information in the way Google does for search results.
What it does apply are alignment criteria such as helpful, honest, harmless (RLHF or Constitutional-AI rules). Those criteria overlap with E-E-A-T’s “trust” dimension, but they are not the same thing.
Phase
Method
E-E-A-T relevance
Training
Predict next word from massive public text data
No E-E-A-T logic involved
Inference
Generate answers based on prompt + internal weights
Doesn’t score credibility like Google does
RAG pipelines
Retrieve text chunks, then generate answer
Retrieval system may favor E-E-A-T-like content
Reinforcement tuning (RLHF)
Human raters judge helpfulness, honesty, etc.
“Trustworthiness” overlaps but isn’t E-E-A-T scoring
Where E-E-A-T does influence LLM output indirectly:
Source selection in RAG (Retrieval-Augmented Generation): When an LLM is used in tools like Perplexity, ChatGPT with browsing, or Claude with documents, it pulls content from sources that search-like algorithms rank. These upstream systems do use E-E-A-T-style heuristics (authority, backlinks, freshness).
LLMs trained on curated corpora (e.g. academic, government, or high-authority sites) inherit some quality biases. Pages with high E-E-A-T are more likely to be in those datasets.
Answer citation models prefer concise, clearly authored, fact-dense paragraphs — a byproduct of E-E-A-T optimization.
What is RAG?
RAG = Retrieval-Augmented Generation
In plain terms, it’s a two-step architecture that lets a language model look things up before it writes the answer—so it can stay factual even when its frozen training data is out of date.
How RAG works
Stage
What happens
Typical tech
Retrieve
A search component (vector DB, BM25, hybrid) finds the most relevant text chunks for the user’s query.
FAISS, Pinecone, Weaviate, Elasticsearch k-NN
Augment
Those chunks are concatenated with the original prompt to form an expanded context window.
Prompt templating
Generate
A language model reads that prompt + evidence and produces the final answer (summary, chat reply, SQL, etc.).
GPT-4o, Claude 3, Llama-3-70B-Instruct
RAG = Retrieval-Augmented Generation
In plain terms, it’s a two-step architecture that lets a language model look things up before it writes the answer—so it can stay factual even when its frozen training data is out of date.
Retrieval-Augmented Generation turns an LLM into a live, citation-backed knowledge assistant. Mastering the retrieve-rank-generate loop—and the guardrails around it—is now core to any production-grade LLM application.
What is RLHF?
RLHF = Reinforcement Learning from Human Feedback. It’s the training phase in which a language model learns how it should answer—“helpful, honest, harmless”-style—by optimizing against scores that come from humans rather than a fixed algorithm.
Large language models that are trained only to predict the next token often:
Produce unsafe or off-topic content.
Ignore user intent (rambling or hallucinating).
Lack a consistent “voice.”
RLHF adds a final alignment layer so the model’s behaviour matches human expectations of usefulness and safety.
Step
What happens
Key artefact
1. Supervised fine-tuning (SFT)
Humans write exemplar dialogues. The base model is fine-tuned on these instruction–response pairs.
SFT model
2. Reward-model training
Annotators read pairs of candidate answers from the SFT model and pick the better one. A smaller “reward model” is trained to predict those preferences.
Reward model (RM)
3. Policy optimisation
Using RL (usually Proximal Policy Optimisation, PPO) the SFT model is updated. It generates answers, the RM scores them, and gradients push the policy toward higher-scoring outputs.