Embeddings in RAG: best models, vs LLMs, OpenAI small vs large, latent space, MongoDB

In RAG, embeddings turn document chunks and questions into vectors so the system can retrieve passages by meaning. Choosing and using them well shapes answer quality more than most other decisions. RAG.NextUpgrad pairs dense embeddings with BM25 and reranking, so this page covers what embeddings do in RAG, the best models, embedding models versus LLMs, OpenAI's small and large models, latent spaces, representations, embedding layers, MongoDB and Office meanings of embedding, and loading web articles.

What is embedding in RAG?

In RAG, embedding means converting each document chunk and each user question into a vector with an embedding model, so the system can retrieve the chunks most similar in meaning to the question before the LLM answers.

The pipeline embeds documents once at indexing time and stores the vectors in an index or vector database. At query time, the question is embedded with the same model and compared with stored vectors to find the nearest chunks. These chunks, often reranked, go into the prompt as context for a grounded answer with citations. Embedding quality, chunk size and metadata filters strongly affect whether the right passages are found.

Which embedding model is best for RAG?

Strong choices include OpenAI's text-embedding-3-large, Cohere Embed, Voyage AI, Gemini embeddings, and open models such as BGE-M3, Qwen3 Embedding and multilingual E5. The best depends on your languages, domain, budget and hosting needs.

Check the MTEB leaderboard's retrieval scores for a shortlist, then test on your own data: write fifty real questions, mark the correct passages, and measure how often each model retrieves them in the top results. Consider maximum input length, dimension size and speed. Multilingual support matters for Indian languages. Open models allow self-hosting for privacy. Whatever you choose, combine it with keyword search and reranking, which often matter as much as the embedding model itself.

What is the difference between an embedding model and an LLM?

An embedding model converts text into a vector representing its meaning, used for search, clustering and classification. An LLM generates text, predicting tokens to answer questions, write and reason. RAG uses both: embeddings retrieve, and the LLM answers.

Also asked as: difference between embedding model and llm

Embedding models are usually smaller and faster, often encoder-based, and trained with contrastive learning so similar texts get similar vectors. LLMs are larger decoder models trained to predict the next token and follow instructions. Some modern embedding models are built from LLMs, then fine-tuned for embeddings. Word embeddings, such as Word2Vec, are even simpler, giving each word one fixed vector, while language models produce context-dependent representations and generate text.

What is the difference between text-embedding-3-small and large?

OpenAI's text-embedding-3-small produces 1,536-dimension vectors and is much cheaper, while text-embedding-3-large produces 3,072-dimension vectors with higher retrieval quality on benchmarks. Both support shortening vectors through a dimensions parameter.

Also asked as: difference between text embedding 3 small and large

For many RAG applications, the small model offers excellent value, especially at scale. The large model helps when retrieval quality is critical, such as legal or technical search where subtle distinctions matter. Shortening the large model's vectors, enabled by Matryoshka-style training, reduces storage while keeping much of its quality, sometimes beating the small model at the same size. Test both on your own queries, measuring recall and total cost, before committing to one.

What is the difference between latent space and embedding space?

A latent space is any internal representation a model learns, such as an autoencoder's compressed code or a diffusion model's image latents. An embedding space is a latent space designed so distances reflect similarity, making it useful for search and comparison.

Also asked as: what is the difference between latent and embedding spaces

All embedding spaces are latent spaces, but not all latent spaces are good embedding spaces. A diffusion model's latent space is optimised for reconstructing images, not for measuring semantic similarity between them. Embedding models are trained with objectives, such as contrastive loss, that explicitly place related items close together. In practice, people use the terms loosely, so check how a representation was trained before relying on its distances for retrieval.

What is the difference between a representation and an embedding?

A representation is any way of encoding data for a model, including hand-crafted features, one-hot vectors or learned vectors. An embedding is a specific kind of representation: a learned, dense, continuous vector, usually mapping discrete items into a space where similarity is meaningful.

Also asked as: what is the difference between representation and embedding

Representation learning is the broader field of learning useful features automatically, and embeddings are one of its most important outputs. Hidden layers inside neural networks also produce representations, which can serve as embeddings if they capture useful similarity. In computer vision, features from a pretrained network act as image embeddings. The distinction matters mostly in academic writing; in engineering, embedding usually means the vector you store and compare.

What is the difference between an embedding layer and an autoencoder?

An embedding layer is a lookup table inside a neural network that maps discrete IDs, such as tokens or user IDs, to trainable vectors. An autoencoder is a complete network that compresses input into a smaller latent code and reconstructs it.

Also asked as: what is the difference between and embedding layer and an autoencoder

Embedding layers learn their vectors through whatever task the network is trained on, such as next-token prediction or recommendation. Autoencoders learn representations by reconstruction, useful for compression, denoising and anomaly detection, and their bottleneck codes can serve as embeddings. Variational autoencoders add a probabilistic latent space used in generative models. An autoencoder can even contain an embedding layer for categorical inputs. They solve different problems despite both producing vectors.

What is the difference between embedding and referencing in MongoDB?

In MongoDB, embedding stores related data inside the same document, such as an order's line items, while referencing stores related data in separate documents linked by IDs, such as orders referencing a customer document.

Embed when related data is read together, has a one-to-few relationship and stays bounded in size, since one query returns everything. Reference when data is shared across documents, grows without limit, or is updated independently, avoiding duplication and the 16 MB document size limit. Many schemas mix both. This sense of embedding is unrelated to vector embeddings, although MongoDB Atlas Vector Search can store vector embeddings inside documents too.

What is the difference between embedding and linking?

In documents and Office files, embedding places a full copy of content, such as a spreadsheet, inside the document, while linking inserts a reference to the original file, so updates to the source appear in the document.

Embedded objects keep documents self-contained and portable but increase file size and do not update automatically. Linked objects keep files smaller and current but break if the source file moves or recipients lack access. On the web, embedding shows content from another site inside a page, such as a video player, while linking sends visitors elsewhere. In AI, embedding has a separate meaning: converting data into vectors for similarity search.

How do you load web articles into RAG for embedding?

Fetch each page while respecting robots.txt and terms, extract the main article text with tools such as Trafilatura or a readability parser, clean boilerplate, split into chunks by headings, add metadata such as URL, title and date, then embed and store.

Also asked as: how to load web articles into rag llm for embedding

Frameworks such as LangChain and LlamaIndex provide web loaders, but dedicated extraction tools usually produce cleaner text. Chunks of a few hundred tokens with small overlaps work well for articles. Deduplicate near-identical pages, and schedule refreshes for content that changes. Keep source URLs with every chunk so answers can cite them, as RAG.NextUpgrad does with cited streaming. Check copyright and licensing before indexing third-party content for products used by others.

Pranjul Rathour

GenAI Engineer · India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.

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