How to Choose Between RAG and Fine-Tuning for Filings

Separate knowledge from behavior

The core distinction: RAG supplies knowledge at inference time by retrieving documents, while fine-tuning changes the model's behavior by adjusting its weights on examples. Filings are knowledge that changes constantly and must be cited, which RAG handles natively. Fine-tuning bakes patterns into the model but cannot inject a filing the model has never seen. If the task is about facts in documents, you almost always want RAG; if it is about consistent output behavior, fine-tuning may help.

Ask whether the failure is the model not knowing a fact or the model formatting an answer wrong. The first is a RAG problem; the second can be a fine-tuning problem.

Default to RAG for filing knowledge

Most filing tasks (answering questions, extracting figures, summarizing sections) need current information from a specific document and a citation back to it. RAG delivers exactly that: it retrieves the relevant passages and lets the model ground its answer in them, traceably. It also adapts instantly when a new filing arrives, with no retraining. For the large majority of filing work, RAG with verification is the right starting point and often the finishing one.

RAG keeps your knowledge in documents you control rather than frozen in model weights. When a filing is restated, you update the store, not the model.

Use the SEC Filing Chunk Optimizer: Pick a filing archetype, tune chunk size and overlap, and see chunk count, embedding cost, and structural-boundary warnings across three chunking strategies.

Consider fine-tuning only for narrow, high-volume format tasks

Fine-tuning earns its keep when you repeatedly extract the same fields in the same structure from many similar documents, and a general model with prompting is inconsistent on format or too verbose. A tuned model can produce tighter, more consistent structured output and may run cheaper per call by needing fewer instructions. But it cannot supply new facts, so it complements retrieval rather than replacing it: fine-tune the extraction behavior, still retrieve the document.

Fine-tuning fixes how the model responds, not what it knows. If your problem is wrong facts, fine-tuning will make the model confidently wrong in a consistent format.

Weigh the cost and maintenance of each path

RAG has ongoing inference cost (retrieval plus generation) and an embedding cost for the corpus, but no training cost and minimal upkeep when documents change. Fine-tuning has an up-front training cost, a dataset-curation burden, and a recurring maintenance cost because a provider model update can require retraining. Compare not just the per-call price but the total cost of ownership including the engineering time to keep a fine-tune current as base models change.

A fine-tune is a liability as well as an asset: every base-model upgrade is a re-tuning decision. RAG inherits model improvements for free.

Use the Financial Document Token Estimator: Paste a 10-K, 10-Q, 8-K or earnings transcript and see token count + one-pass extraction cost across ten frontier LLMs, with cache-hit toggle.

Compare against a long-context baseline

Before committing to either, test whether simply passing the relevant filing into a long-context model with a good prompt meets the bar. It avoids retrieval errors and fine-tuning maintenance entirely, at the cost of more tokens per call. For low-to-moderate volume on single documents, long context is often the simplest adequate answer. RAG wins when you query many documents or need to control cost at scale; fine-tuning wins only on top of one of these for format consistency.

Long context is the simplest option and a fair baseline. If it meets your accuracy and cost bar, you may not need RAG or fine-tuning at all.

Use the Model Selector for Finance: Input task, latency budget, cost budget, context size, and quality sensitivity; get ranked model recommendations with rationale — grounded in published.

Verify regardless of which you choose

No method removes the need to verify numbers and citations. RAG can retrieve the wrong passage; a fine-tuned model can transcribe a figure wrong in a perfectly formatted output; a long-context model can lose a number in a large input. Whatever you pick, keep the per-number verification and citation-faithfulness checks on top. The method choice optimizes cost and consistency; verification protects correctness, and that is non-negotiable in finance.

The verification layer is the same no matter which method you choose. Build it first so the method decision is about cost and consistency, not safety.

Use the Hallucination Detector: Paste a source document + an LLM's extraction. Every numeric claim in the output is checked against the source. Client-side. Catches silent fabrication.