Extending AI’s Memory: RAG, CAG, Long Contexts and Vector Search

Large Language Models (LLMs) have an incredible ability to generate text and answer questions, but they don’t always know what they need to know. By default, an LLM is bound by the information in its training data, which might be outdated or irrelevant to a specific query. This can lead to confident but incorrect answers, a phenomenon known as hallucination, where the model “makes up” facts that sound plausible but aren’t true. To tackle this, the AI community has developed strategies to give LLMs better grounding in real, up-to-date data. Two key approaches have emerged:

• Retrieval-Augmented Generation (RAG): fetching relevant information on the fly from an external knowledge source.
• Cache (Context)-Augmented Generation (CAG): pre-loading a lot of reference information into the model’s context window so it’s readily available during generation.

Both methods aim to extend the “memory” of AI systems and reduce hallucinations, but they do so in different ways. Alongside these, new tools like vector databases (for efficient similarity search) and batch processing APIs (for scaling up tasks) are empowering developers to build AI solutions that are faster, more accurate, and more scalable than before. This article breaks down how these pieces fit together, from long context windows that fit whole documents to vector math that finds the right info, and what it means for real-world AI deployments.

Retrieval-Augmented Generation (RAG): Letting AI Look Things Up

RAG is like giving the LLM a clever librarian. Instead of trusting the model’s training data alone, we provide it with up-to-the-minute information relevant to the user’s query. Here’s how it works in practice:

1. Question or Query: A user asks a question or makes a request.
2. Vector Search: The system translates the query into an embedding (a numerical representation of the query’s content and meaning) and searches a vector database for similar embeddings among a collection of documents or facts. The search uses measures like cosine similarity to find text chunks that are semantically related to the query, essentially looking for the closest match in meaning within a high-dimensional embedding space.
3. Retrieve Top Documents: The most relevant pieces of text (e.g. paragraphs from an internal knowledge base or documents) are retrieved. Each piece comes from your private data. For example, your company’s documentation or a set of articles, that the LLM wouldn’t otherwise know from its public training data.
4. Augment the Prompt: Those retrieved chunks are added to the LLM’s input prompt, along with the user’s question. The prompt might be structured like: “Here are some relevant documents: … [document excerpts] … Now answer the question: [user query].”
5. Generate Answer: The LLM processes this augmented prompt and produces a response that hopefully stays true to the provided information.

By grounding the LLM’s response in relevant retrieved data, RAG can improve accuracy and reduce hallucinations. The model isn’t forced to “fill in the blanks” from memory alone; it has the facts on hand. This approach has proven powerful, it enables things like question-answering over your proprietary database, or a customer support chatbot that references actual policy documents in its answers. Crucially, RAG can do this without needing to retrain the entire model for each new piece of information, which makes it flexible and cost-effective.

But, RAG comes with complexity. There are multiple moving parts: you need to parse and chunk your documents, generate embeddings for them, maintain a vector index, and handle the search and retrieval efficiently. Each step is a potential point of failure or added latency. If the vector search is slow, the user waits. If your documents aren’t chunked well (too large or too small), the model might get irrelevant or incomplete context. And because the retrieved text has to fit into the LLM’s input size limit, there’s a constant juggling act of selecting just the right pieces and not overloading the prompt.

RAG shines for dynamic or large knowledge bases. If your data is always growing or changing (e.g., new blog posts, daily support tickets, financial reports), RAG lets the model access the latest info. It’s like long-term memory, the system can scan “millions of documents to pull what matters now” on demand. RAG is also invaluable when the total information is far too much to fit into any one model context. For enterprises dealing with millions of pages of data, you still need search and retrieval to narrow down what the model should see.

Cache-Augmented Generation (CAG): Using Long Context as a Cache

CAG takes a different approach: instead of retrieving relevant info at query time, why not load a bunch of relevant info ahead of time into the model’s context, and let the model “read” it all in one go? Essentially, treat the LLM’s extended context window as a cache of knowledge that persists across interactions. Here’s what that means:

• Before a user even asks a question, you feed a large static dataset or knowledge base into the model as part of the prompt (or some initialization step). For example, you might prepend a whole product manual, or a synopsis of an internal wiki, or any documents that you expect the user will ask about.
• The model processes this information internally, effectively building an internal understanding of it. Importantly, CAG stores not the raw text, but the model’s internal representation (memory) of that text. This is like telling the model: “Here’s everything you should know about our products. Memorize it (for now).”
• When a user query comes in, the system doesn’t need to fetch anything externally. It can answer using the context it has “cached” in the prompt or in the model’s memory from the earlier input. The response is generated with that rich context already in mind.

This eliminates the real-time search step, meaning responses can be faster and use less compute at inference time (since you’re not re-reading documents for each query. Infrastructure-wise, CAG can be simpler: no vector database or search component is needed during runtime, because you did the heavy lifting of selecting and loading the knowledge in advance. In a sense, CAG trades latency for context window usage, you might spend more tokens up front to include a lot of data in the prompt, but avoid the delay of retrieval later.

The obvious limitation of CAG is the size of the context window. Today’s models have context limits that, while growing, are still finite. Even though state-of-the-art models boast windows of tens or even hundreds of thousands of tokens (OpenAI’s GPT-4 offers up to 32k, and Anthropic’s Claude 2 goes up to 100k tokens), that’s nowhere near enough to literally preload everything for large enterprises. Claims of ultra-huge context (there have been headlines about models like Google’s Gemini or Meta’s LLaMA exploring 1-2 million token contexts) remain more aspirational than practical right now. In real deployments, using 32K to 100K tokens effectively means carefully curating what you put in. If you have a thousand-page manual, you might fit it, but millions of pages of company data won’t all sit in one prompt. So CAG works best when the knowledge base is “small” or static enough to fit in the context, or when you can predict which subset of information is needed for the task at hand.

Another limitation is that more isn’t always better: even if you can stuff a huge document into a prompt, the model’s performance might actually drop if the context is too unwieldy. Important details could get “lost” in the crowd of tokens, the model might skip over or misinterpret some parts when processing a very long input. In many cases, breaking documents into chunks and retrieving selectively (the RAG way) can yield more accurate results than a brute-force dump of data into context. In essence, CAG doesn’t eliminate the need for smart information selection; it just moves that selection to an upfront process of choosing what to cache.

When to use CAG: CAG is attractive for low-latency applications and relatively static knowledge. For example, a FAQ bot that always refers to the same product manual could use CAG: load the manual into a prompt once and answer questions from it quickly, rather than searching the manual anew each time. If you’re building a customer support assistant that needs to be very fast (say, on a website live chat), avoiding a vector search can save precious time. CAG is also useful in scenarios where your context window can actually hold the majority of what’s needed, for instance, a personal notes assistant working off your 100-page notebook might just preload it all. It shines when latency and simplicity are more critical than handling gigantic or highly dynamic data.

In practice, many real-world systems blend RAG and CAG to get the best of both. You might use RAG to retrieve a focused set of documents (say the top 5 relevant pages), and then use CAG by caching those pages’ content or the model’s digest of them for subsequent queries. As one engineer nicely put it, RAG is like an AI’s long-term memory (search broadly when needed), and CAG is like short-term memory (remember the recent or important things closely). Used together, RAG finds the signal and CAG keeps it close, a powerful combination for handling both breadth and depth of knowledge.

The Rise of Long Context Windows

One reason CAG is even feasible now is the dramatic expansion of context windows in cutting-edge LLMs. Early GPT-3 models had context limits around 4K tokens (which is a few pages of text). Today, we have models that can take orders of magnitude more:

• OpenAI GPT-4 (2023): up to 32,000 tokens context length.
• Anthropic Claude 2 (2023): around 100,000 tokens.
• Google Gemini family (2024/2025): rumored and prototype models pushing possibly into the hundreds of thousands or more.

To put that in perspective, 100k tokens is roughly 75,000 words, about the length of a novel or several hundred pages of text. This means a model like Claude 2 could, in theory, accept an entire book as input and still answer questions referencing any part of it. Google’s Gemini, which is Google’s next-generation foundation model, is expected to leverage such large contexts (and multimodal input, not just text) to allow feeding in large amounts of data like images, code, or documents in one go. In fact, some reports have hinted at experimental versions with over a million or two million token contexts, though practical usage of that is yet to be seen.

Why does context size matter? Because the more information you can pack into a single query for the model, the less often you need to resort to external retrieval. A long context window can act as a temporary knowledge base. But as noted, even 100K tokens can be small compared to a company’s entire data repository. So while context expansion is exciting and will enable new use cases (like analyzing long-form content or doing complex multi-document reasoning in one shot), it doesn’t completely remove the need for retrieval and other clever data management. It’s a bit like computer RAM, even if you get more of it, you’ll fill it up if your workload grows; and you still need a hard drive (database) for the overflow.

For developers and businesses, the trend of growing context means fewer constraints when designing prompts and workflows. You can afford to include more history in a chatbot conversation, or give the model more reference text for disambiguation. It simplifies some aspects of prompt engineering, you don’t have to truncate aggressively or condense context as much. But it also raises new questions: how do we best utilize those extra tokens? It’s still costly (financially and computationally) to process huge prompts. And just dumping raw data might not yield the best results without summarization or highlighting key points. This is where techniques for manipulating data for long contexts come in. We might need to intelligently shorten documents, skip irrelevant sections, or annotate the context so the model pays attention to the right details. In other words, as context windows grow, so does the importance of strategic data formatting and context management, deciding what and how information is included in that giant prompt.

Taming Hallucinations with Grounding and Retrieval

We touched on hallucinations earlier, the tendency of an LLM to output incorrect information with great confidence. Why does this happen? At its core, a language model generates the next word by looking at patterns in training data, not by consulting a fact database. If the prompt asks a question and the answer isn’t clearly encoded in those patterns (or if the model doesn’t “realize” which facts apply), the model will fabricate a plausible-sounding answer. It’s not lying intentionally; it’s doing its best to continue the text in a way that seems coherent. Sometimes, the made-up answer even looks very detailed and factual (like citing a fake article or statistic) because the model knows the style of correct information, even if the substance is false.

This is obviously a problem for applications where correctness matters (which is most applications, outside of maybe creative fiction). No business wants an AI assistant that invents product features or gives customers wrong instructions. Retrieval-augmented generation (RAG) was introduced largely to fight this issue. By grounding the model’s input with real data, we give the model fewer chances to go off-script into imagination. In theory, if the model has the relevant facts in front of it, it should incorporate them into the answer, leading to responses that are more accurate and supported by source material. Indeed, many find that a well-implemented RAG system drastically cuts down on hallucinated content because the model will tend to copy or summarize the retrieved text, which comes from a trusted source, rather than guessing.

RAG isn’t a magic wand that eliminates hallucinations completely. The model could misinterpret the retrieved context or not use it effectively. It might combine a retrieved fact with something from its own memory incorrectly. And if the retrieval brings back irrelevant or low-quality text, the model’s answer can only be as good as that input. In practice, teams use additional techniques to further reduce hallucinations:

• Better data curation: Make sure the knowledge base or documents you feed into RAG are accurate, up-to-date, and relevant. If the underlying data has errors, the model can still pick those up (garbage in, garbage out).
• Prompt instructions: Sometimes explicitly instructing the model like “Use the provided information to answer and don’t add anything else” can help it stick to the source.
• Fact-checking or verification steps: Advanced pipelines might double-check the model’s answer. For example, after the model answers, you could have a second step where you search the knowledge base for statements from the answer to verify them, or use another model to critique the answer.
• Citing sources: There’s an emerging practice of having LLMs output citations or references for their statements. If the model is forced to show where an answer came from (like providing a document ID or quote for each fact), it’s less likely to inject unsupported claims. This is tricky to implement reliably, but it’s an active area of research and tooling.
• User in the loop: For high-stakes outputs, having a human reviewer or giving the end-user the source text to verify can mitigate harm from any hallucinations that slip through.

In summary, hallucination is a fundamental quirk of how LLMs work, but by grounding them with retrieval (RAG) and using smart policies, we can greatly reduce the incidence of made-up information. As the K2View blog succinctly put it, RAG “allows the LLM to anchor its responses in actual data, reducing the risk of fabricated or whimsical outputs”. And as an extension, Cache-Augmented Generation (CAG) can also help if your cached context is filled with verified facts, the model is essentially “reading” a mini knowledge base that you control, rather than free-wheeling.

Vector Databases and Cosine Similarity: How AI Remembers Where Facts Are

A core component under the hood of many RAG systems is the vector database. This is the technology that lets us store and retrieve embeddings, those numerical representations of text efficiently. But how does it actually help find relevant information?

When we ingest documents (say, a collection of company FAQs or technical manuals) for use in an AI system, we typically break them into chunks (maybe a few sentences or a paragraph each). Each chunk of text is passed through an embedding model, essentially another AI model, often smaller, whose job is to convert text into a list of numbers (a vector). This vector might be hundreds or thousands of dimensions long, and it’s designed so that texts with similar meaning end up with vectors that are close together in this multi-dimensional space. Think of it like mapping text into a galaxy of points: a statement about pricing plans and another statement about costs might end up in the same neighborhood of that space.

The vector database stores all these vectors along with references (like an ID or metadata) to the original text chunk. Now, when a user question comes in, we do the same embedding process on the query, producing a vector for the question. The vector DB then performs a similarity search: it quickly finds which stored vectors are closest to the query vector. One common way to measure “closest” is by using cosine similarity, which effectively measures the angle between two vectors. Two vectors pointing in very similar directions (small angle) have a cosine similarity near 1, meaning the query and that document chunk are likely talking about related things. By contrast, if the vector for the query is nearly perpendicular to another vector (cosine ~0), it means they share little semantic overlap.

Under the hood, modern vector databases use clever algorithms (like HNSW graphs or product quantization) to make this search fast, even if you have millions of vectors stored. Instead of checking every single vector, these algorithms zero in on promising candidates. It’s a bit like how Google Maps might not check every city when you search for “coffee shops” but narrows down to the region you’re in. The result is that even very large datasets can be queried in a fraction of a second to retrieve, say, the top 5 most similar pieces of text to the query.

After retrieving those top candidates, the system then has the actual text for those chunks, and that’s what we feed into the LLM’s prompt (as described in the RAG section). The whole process, embedding the query, vector search, retrieving text, is usually wrapped in libraries or frameworks nowadays (like LangChain, LlamaIndex, or cloud services) so developers don’t have to implement it from scratch every time. But understanding it conceptually is important for tuning your system. For example, you might adjust the embedding model if the results aren’t relevant enough (there are domain-specific embedding models for code vs. legal text, etc.), or tweak the chunk size and metadata stored to improve search accuracy. Some vector DBs also let you filter by metadata, e.g., only search in documents of a certain type or date range, which can dramatically improve relevance if used right.

To illustrate, imagine you have an internal Q&A system for an e-commerce company. A user asks, “What is the warranty on product X if I purchase it in Europe?” The system will embed that question and search in the vector DB where you’ve stored all product manuals and policy documents. The cosine similarity might surface a chunk from the “Product X – Warranty” section of the manual, and maybe another chunk from a “European purchases policy” document. Those pieces get pulled out, and then your LLM sees them and can answer with something like, “Product X comes with a 2-year warranty in Europe, as per the warranty terms (and maybe it cites the policy).” The heavy lifting of finding the right info was done by the vector similarity search.

The Power of Batch Processing (Google Gemini’s Batch API)

So far we’ve focused on how an AI system handles one query at a time when a user asks something, the system finds info and responds. But what about situations where you need to process lots of data or requests in bulk? For example, say you have 10,000 customer reviews and you want an AI to summarize each one, or you have an entire database of product descriptions that you’d like rewritten in a more engaging tone. Doing this one prompt at a time could take ages and might not be efficient.

This is where batch processing capabilities come in, and Google’s Gemini Batch API is a notable example of this emerging feature. Google’s Gemini is a family of advanced models (the successors to PaLM) that are not only powerful in understanding text (and potentially images, etc.), but also come with tooling to handle large-scale jobs. The Gemini Batch API allows you to send a large number of prompts or inputs to the model in one go, as a batch, and have the results processed asynchronously and delivered when ready.

Why is this useful?

• Efficiency & Throughput: By handling many requests in parallel on the backend, the system can achieve higher throughput than sending each request individually. Google even offers cost incentives, batch requests for Gemini are half the cost of regular calls per token. This makes it extremely cost-effective when you have big volumes to process.
• Bulk Content Generation: Imagine an online bookstore with thousands of books lacking descriptions. Instead of a human writing each one or prompting an AI manually one by one, you can prepare a batch job where each book’s details (title, genre, key points) are fed as a prompt to the AI to generate a description. The Gemini batch system will churn through them, and perhaps within a couple of hours, you get a thousand polished descriptions in your output storage. This beats doing it interactively which might be rate-limited or time-consuming.
• Consistency: When processing in batch, you can ensure the same prompt style or parameters apply to all, resulting in a uniform style across outputs. For instance, all those book descriptions will follow a similar tone, which is great for branding.
• Offline Processing: Batch jobs typically run asynchronously, you don’t sit there waiting for a response to come back in a chat window. Instead, you might get the results written to files or a database (Google’s API can output to Cloud Storage or BigQuery tables). This is handy for large jobs where you can fire-and-forget and then collect results later. It doesn’t block a user session.
• Multimodal & Complex Input: The Gemini Batch API is also built to handle multimodal prompts in batch. For example, maybe you want to feed an image and a text caption together for each request (like “here’s a product image, generate an ad blurb for it”). The batch can handle those complex inputs as JSON lines and process them all the same. This opens up possibilities beyond just text-to-text tasks.

One thing to keep in mind: batch processing is ideal for back-office or batch workflows, things that don’t need an instant response to a user. It’s not suitable for real-time queries (you wouldn’t batch your interactive chatbot requests, for instance, because the user expects a quick reply). It’s more for, say, overnight jobs, data pipeline steps, or massive one-off tasks like migrating content. Also, batch jobs run on “spare capacity”, meaning they might queue up and execute when resources free up, and have certain time limits (Google’s docs note a job might take up to 24 hours if it’s huge, with up to 72 hours including queue time). So it’s not instant, but it’s robust for large scale.

Other AI providers also have or are introducing batch processing, it’s becoming a standard feature as customers want to apply AI at scale to their data, not just single queries. If you’re an engineer at a non-tech company thinking “We have all this data, how do we leverage AI on it?”, these batch capabilities mean you can plug AI into your data pipelines. For example: feed all your sales emails from last year and have an AI analyze them for customer sentiment trends in one batch job. The bottom line is, the batch API makes AI a practical bulk data tool, not just an interactive assistant.

Other Emerging Tools and Techniques

The landscape of AI is moving fast, and beyond RAG, CAG, vectors, and batch processing, there are a few other technologies and best practices worth knowing:

• Model Ensembling & Specialization: Sometimes using one model isn’t enough. As we saw in the code review example from my CodeBot project, they used Google Gemini for broad architectural understanding and Anthropic Claude for detailed code analysis, combining strengths. In general, you might use one model for one task and another model for a different task in the same workflow (one might be better at summarization, another at creativity, etc.). Or run two models and have one double-check the other. This multi-model approach can yield better results than any single model alone.
• Prompt Chaining & Agents: Frameworks like LangChain popularized the idea of chaining prompts together, where the output of one step becomes input to the next. For complex tasks, an AI might benefit from breaking the problem into parts. An agent approach might involve the model deciding it needs to perform a search or a calculation and then using a tool for that. For example, if asked a complicated question, the system might first use RAG to gather data, then ask the LLM to analyze it, then maybe use another prompt to format the answer. These chained workflows are how a lot of sophisticated AI applications (like multi-turn assistants or AutoGPT-like systems) operate under the hood.
• Fine-Tuning and Custom Models: Instead of always using a giant general model and augmenting it, there’s also the route of fine-tuning or training a smaller model on your specific data. For instance, if you have a fairly static but domain-specific knowledge base, you could fine-tune a model to know that content. Fine-tuning can make the model internalize the info (reducing reliance on retrieval), but it requires expertise and isn’t as flexible for dynamic data. It can be used in tandem with RAG: e.g., fine-tune a model to better understand the style of your documents and then still use retrieval for factual updates.
• Knowledge Graphs and Symbolic Integration: Not all data is best represented as raw text. Some companies maintain structured knowledge bases or ontologies of facts (triples like “Product X – has feature – waterproof”). There’s an evolving space of combining these knowledge graphs with LLMs, so the model can query the graph for precise facts or use the graph to verify consistency. This is another way to mitigate hallucination, ensure certain answers come straight from a database or graph where truth is guaranteed.
• Guardrails and Validation: For enterprise use, having guardrails is crucial. This includes things like content filters (to catch inappropriate outputs), validation steps (e.g., if the model produces a number as an answer, cross-verify it’s in a reasonable range), and user feedback loops. Tooling like Azure’s AI Guardrails or open-source projects allow setting some rules for the model’s output format and content. While not directly related to RAG or CAG, these ensure that even if the model drifts, it doesn’t produce something unacceptable or nonsensical to the end user.
• Monitoring and Analytics: With systems that have many pieces (vector DB, LLM, etc.), monitoring becomes important. Observability tools are coming up that track things like embedding search performance, how often the model didn’t use the retrieved info, or where the model might be hallucinating. For example, Datadog’s LLM monitoring or other ML observability platforms can detect spikes in hallucination-like behavior and help developers refine prompts or retrieval strategies

All these pieces underline a common theme: building a successful AI application is not just about having a smart model, but about orchestrating data, prompts, and processes around the model. For a non-AI company, this may sound daunting, but thankfully many cloud providers and open-source tools encapsulate these techniques. The key is understanding the problem you’re trying to solve (search vs. Q&A vs. generation) and picking the right mix of approaches. Need factual accuracy? Lean on RAG or structured data. Need speed on a defined dataset? CAG with a long context model might do. Need scale? Batch processing and efficient vector search come into play.

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AI is no longer a black box that either knows something or doesn’t. With approaches like RAG and CAG, we can significantly extend what an LLM can effectively “know” at query time, either by looking outward to fetch information or by packing information inward into its context. Long context windows are giving models a bigger built-in “scratch pad” for knowledge, while vector databases and cosine similarity provide the intelligent lookup mechanism to find needles in haystacks of data. These technologies complement each other: a vector database finds the right info, and a large-context model can take more of that info in at once to reason about it.

For businesses and developers, these advances mean that even if you’re not a tech giant, you can leverage state-of-the-art AI on your own data. Your customer support bot can cite the actual policy manual sentence rather than guessing. Your marketing team can auto-generate content en masse with consistent tone. Your data analysts can ask questions in natural language and get answers that draw upon the thousands of documents your company has accumulated. It requires some engineering, setting up the pipelines and selecting tools, but the capabilities are more accessible than ever.

isn’t “should we use retrieval or a bigger model?”, it’s often wise to use both. As one research piece noted, it’s not about RAG versus CAG, but RAG and CAG working together. And whether you call it “cache” or just “long context,” the idea is the same: we want our AI assistants to have both a short-term working memory and access to a long-term knowledge base. By engineering our systems with these building blocks, we inch closer to AI that’s not only fluent, but also informed and reliable.

Staying educated on emerging tech, from new APIs like Google’s Gemini, to better embedding models, to techniques for reducing hallucinations, is crucial. The AI field in 2025 is evolving quickly, and what’s cutting-edge today (say 100k token contexts or cheap batch OCR) can become standard tomorrow. For any team looking to infuse AI into their products, a mix of curiosity and pragmatism goes a long way. Understand the tools, experiment with what they can do, and always keep the end-user experience in focus. The goal is an AI that genuinely helps, without the hiccups, and with the right combination of retrieval, context, and data savvy, we’re getting closer to that goal.

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