Embedding RAG into your application: giving AI the context it lacks
Embedding RAG into an application means converting your documents into passages and vectors once, retrieving the few passages relevant to each question, and putting them in front of the model before it answers - so it responds from your material instead of from a plausible guess.
What embedding RAG actually means
It means your application answers from your own material. A model on its own knows what was in its training data and nothing else, so a question about your refund policy, your onboarding process or last quarter's numbers gets a fluent invention. Adding retrieval changes the shape of the request: before the model answers, your application looks up the handful of passages that bear on the question and includes them in the prompt. The model stops recalling and starts reading.
The four pieces, and which one people get wrong
A RAG feature is conversion, storage, retrieval and prompting. Conversion turns documents into passages and each passage into a vector. Storage keeps those vectors somewhere searchable. Retrieval finds the nearest few to a question. Prompting puts them in front of the model with an instruction to answer only from what it was given. Teams reliably spend their effort on the last two, because that is where the interesting code is, and reliably lose to the first one - a corpus with mangled tables and missing scans cannot be rescued by any amount of prompt engineering downstream.
Convert once, retrieve many times
Conversion is the expensive step and it is the one you do least often. A document is parsed, chunked and embedded once when it arrives, and then read thousands of times. This is why conversion belongs in a pipeline rather than in a request handler: it runs on upload, or on a schedule, or when a source system signals a change - never while a user waits. Retrieval, by contrast, has to be fast enough to sit inside a request, which is a vector search over data you have already prepared.
What to store beside the text
Store enough to trace an answer back. Each passage wants the source file it came from, its position within that file, and how the text was obtained - parsed from a text layer, transcribed by OCR, transcribed from speech, or described by a vision model. That last one matters more than it looks. A description is inference and can be confidently wrong in ways a transcription cannot, so an application that cites its sources needs to know which it is holding before it presents a passage as a quotation.
Chunk boundaries decide answer quality
The most common cause of a RAG feature that feels unreliable is chunking, not the model. A passage cut mid-sentence produces a vector describing a fragment whose subject is in the previous chunk, so retrieval returns something that matched the query but cannot answer it. Cut on paragraph and sentence boundaries, overlap consecutive chunks so a fact spanning a boundary survives whole somewhere, and size the chunks for the embedding model rather than for the language model's context window.
Budget for the retrieval, not just the tokens
The cost model of a RAG feature has two halves that people conflate. Conversion is a one-off per document and scales with how much material you have. Inference is per question and scales with traffic and with how many passages you include. Retrieving twenty passages instead of five rarely improves the answer and reliably quadruples the prompt, so the practical tuning knob is how few passages you can include and still be right.
Where the data goes is an architectural decision
Some material cannot leave the machine it is on - identity documents, medical records, contracts under NDA, anything covered by a data residency clause. That constraint decides where conversion runs long before it decides which model you call. Conversion in the browser or on your own hardware keeps the document local and produces a corpus you own; a hosted API is the right answer when the volume is high and the material is not sensitive. Deciding this early is cheaper than retrofitting it, because it determines where the pipeline lives.
How to tell whether it is working
Evaluate retrieval separately from generation. Take real questions, note which passage genuinely answers each, and measure how often retrieval returns it at all. If the right passage is not in what you retrieved, the model was never going to answer correctly and no prompt change will fix it - the fault is in conversion or chunking. Only once retrieval is reliably finding the right material is it worth tuning how the model uses it.