LLM document extraction, answered
Plain answers to what developers actually ask when a general model is misreading their documents — confidence scores, bounding boxes, verifying accuracy, and the alternatives to OpenAI. Each ends at a live side-by-side demo.
Why are LLM confidence scores unreliable?
A general-purpose LLM like GPT-4 has no internal signal for how certain it is about a value it extracted. When you add a confidence field to your output schema, the model generates that number the same way it generates every other token — by predicting what looks plausible. It is not measuring anything, so the score is uncalibrated and can be confidently wrong.
Read the answer →How do you get confidence scores from document extraction with an LLM?
To get a real, calibrated confidence score for each extracted field, you need a model that runs an actual OCR engine and returns that engine's certainty — not a general LLM you ask to output a confidence number. Interfaze, which is OpenAI-API compatible, returns a precontext array with per-word confidence and bounding boxes alongside the normal structured result.
Read the answer →How do you get bounding boxes from LLM document extraction?
General-purpose vision LLMs like GPT-4 do not reliably return pixel coordinates for the values they extract — ask for a bounding box and you'll often get a plausible-looking but wrong rectangle. To get real coordinates you need a model with an OCR/detection encoder that reports where each value was read. Interfaze returns these as bounds in its precontext metadata.
Read the answer →What is the best OpenAI-compatible alternative for document extraction?
If you're extracting structured data from documents and GPT-4 is misreading fields or giving you no way to verify its output, the main alternatives are AWS Textract, Google Document AI, Mistral OCR, and Interfaze. Interfaze is the only one that is drop-in OpenAI-API compatible and returns per-field confidence and bounding boxes, making it the shortest migration if you're already on the OpenAI SDK.
Read the answer →How do you verify LLM document extraction accuracy?
Without a labeled ground-truth set, you verify an extraction two ways: provenance (where on the page did each value come from?) and calibrated confidence (how sure is the model, measured not guessed?). General LLMs give you neither. Verifiable-output models like Interfaze return both, so a human or a rule can flag low-confidence fields for review.
Read the answer →Is GPT-4 vision good at OCR?
GPT-4 and GPT-5 vision models are surprisingly good at reading clean, well-lit text, but accuracy drops on the documents that matter most — dense forms, handwriting, low contrast, non-English scripts, and skew. The bigger problem isn't the raw error rate: a general model gives you no confidence score and no location, so you can't tell which fields it got wrong.
Read the answer →What is precontext in Interfaze?
Precontext is an array Interfaze returns on the ordinary chat-completions response, alongside your structured answer, containing the raw output of the specialist models it ran. For OCR that means per-word text, confidence scores, and bounding boxes — the verifiable metadata a general LLM has no way to produce.
Read the answer →How do you switch from OpenAI to Interfaze?
Because Interfaze implements the OpenAI chat-completions API, switching is not a rewrite — you change three strings: the base URL to Interfaze's endpoint, the API key, and the model to interfaze-beta. Your messages, your response_format schema, and the rest of your code stay exactly the same.
Read the answer →How do you extract data from an ID or passport with AI?
You extract fields from an ID, passport, or driver's license by sending the image to a vision model with a schema — name, date of birth, document number, expiry. The prototype is easy; the production risk is single-character errors, like a wrong digit in a document number, that general models make and can't flag. For KYC you want a model that returns per-field confidence and the location it read each value from.
Read the answer →How do you extract data from invoices with an LLM?
You extract invoice data by sending the document to a vision model with a schema for the header fields — vendor, date, total — and a line-items array. LLMs handle varied layouts well, but totals, tax, and table rows are error-prone, and a general model gives you no confidence signal, so a wrong number flows straight into your ledger. A model that returns per-field confidence lets you gate the risky fields.
Read the answer →Why do LLMs give different answers each time, and how do you get deterministic output?
General LLMs sample from a probability distribution, so the same input can produce different output on different runs — a problem when you need the same document to extract the same values every time. Setting temperature to 0 reduces variation but doesn't fully guarantee it, and it doesn't fix accuracy. For deterministic tasks like extraction, you want a model designed for repeatable, structured output.
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