Building Research Workflows with Web Archives and LLMs

Large language models are powerful research assistants — they summarize documents, extract structured data, compare sources, and suggest related queries. But applying LLMs to web archives requires care. Archives are messy, metadata is inconsistent, and LLM hallucinations can introduce false claims into otherwise solid research. The key is designing workflows that leverage LLM strengths while preserving verification and rigor.

Start with retrieval, not generation. Use traditional search (keyword or semantic) to identify candidate documents from the archive. Only then apply an LLM to summarize, extract, or compare. This grounds the model in real sources rather than letting it fabricate plausible-sounding content. Think of the LLM as an assistant that works on documents you hand it, not as a search engine itself.

Chunking matters. Web archives contain long PDFs, multi-page snapshots, and verbose meeting minutes. Break documents into logical sections (by heading, by page, by speaker) before feeding them to an LLM. This improves summary quality, reduces context overflow, and makes it easier to trace which section produced which claim.

Always cite the source artifact, not the LLM output. If an LLM summarizes a city council meeting transcript, your citation should reference the original transcript with the snapshot date and archive URL — not "GPT-4 summary, accessed Nov 2025." The model is a tool in your workflow, not a source itself.

Use LLMs for structured extraction. Example: feed a batch of planning commission PDFs into a model with a prompt like "Extract: meeting date, project name, vote outcome, key objections." Return results as JSON. This turns unstructured archives into queryable datasets without manual data entry. Validate a sample of outputs to catch systematic errors.

Arkibber is designed to integrate cleanly with LLM workflows: normalized metadata reduces preprocessing, consistent formatting improves extraction accuracy, and fast filtering lets you assemble document sets that match LLM context windows. Search, filter, download a batch, process with an LLM, and validate — a full cycle in minutes rather than hours.

Build verification into every step. When an LLM extracts a claim, have it return the specific sentence or paragraph that supports it. Cross-check extracted dates against known metadata. If the model says "the budget passed in March 2019," verify that claim against the original document before publishing.

For exploratory research, use LLMs to generate query suggestions. Feed a model a few representative documents and ask: "What other topics or entities should I search for?" This helps surface adjacent material you might not have considered. Follow up with traditional search to validate that the suggestions actually exist in the archive.

Batch processing workflows: collect 50-100 PDFs on a topic, run a standardized extraction prompt across all of them, compile results into a spreadsheet, then manually review outliers and anomalies. This hybrid approach (automated extraction, human validation) scales well and catches LLM errors before they propagate.

Privacy and ethics: LLMs often send data to external APIs. Before uploading archived documents, verify they are public records and that your use complies with terms of service. For sensitive material, use local models or on-premise deployments that do not phone home.

Looking ahead, expect more archives to offer LLM-friendly APIs: pre-chunked documents, clean text extraction, structured metadata, and embedding support. This will make retrieval-augmented generation (RAG) workflows standard for historical research. Early adopters who build these pipelines now will have a significant advantage.

Final principle: LLMs amplify your judgment, they do not replace it. Use them to move faster, cover more ground, and spot patterns — but always verify, always cite primary sources, and always be transparent about where machine assistance ends and human analysis begins.