Introducing the most Advanced Rich, Granular Citations: A Step Toward More Trustworthy AI
We are introducing a new multi-layered citation mechanism designed for agentic workflows — built to bring instant traceability and trust to every AI-generated insight.
At Lampi AI, our customers rely on AI Agents to conduct deep research and complete complex workflows — from due diligence and market analysis to full company report generation. Our AI agents often output structured reports from hundreds of files and web searches.
In production, when you're reviewing a late-night analysis or checking the foundation of an investment memo that includes multiple source files, you don’t have time to open every document or dig through data you’ve never seen before. You want one-click trust, seamless verification, and citations that just work — across files, formats, and languages.
💡 That’s why we’ve been focusing on building a citation system that delivers instant traceability, sentence-level accuracy, and effortless navigation — so every AI-generated insight can be trusted at a glance.
Why standard citation systems fall short
Today, most Large Language Model’s (LLM)-based architectures using knowledge-based processes allow the generation of answers with a reference or citation to the source content, in particular in AI web-browsing and Retrieval Augmented Generation (RAG) applications. This well-known process – which has evolved into more advanced agentic mechanisms – is especially effective in enterprise use cases where there is a need to retrieve proprietary knowledge.
Citations enhance trust and reduce hallucination risks, providing an additional layer of trust and enabling users to see exactly which source a given part of a response is based on external data. They provide a direct link between the AI-generated answers and the underlying data that the model has used. While citations cannot guarantee that an AI answer is always correct, they do allow users to quickly verify the accuracy of a response. Without reliable citations, users are left to manually cross-reference model outputs with original documents, a tedious and error-prone process. Worse, if they trust the AI without verification, erroneous data could inform key business decisions or actions.
Multiple citation mechanisms, including post-hoc methods, display sources in formats like hyperlinks, footnotes, or traditional in-text references, allowing users to trace a response back to its original source (e.g., a document or webpage).
But building a trustworthy and verifiable AI system is not as simple as quoting a document or linking to a file, or asking the LLM to return exact quotes in a prompt. The existing approaches expose notable limitations: RAG often compromises answer quality due to incomplete or mismatched context, while post-hoc methods introduce latency due to their added complexity. Worse, generated citations frequently point to entire web pages or loosely chunked snippets, forcing users to manually verify the specific source behind each answer (Weitao Li et al, 2024).
All users that interact with files or the web using AI know these problems all too well:
- “Interacting with files or websites in AI is tough — I always have to double-check it’s not a hallucination.”
- “The sentence in my AI-generated answer references a long webpage… now I need to dig through it.”
- “The citation points to a chunk from the wrong file — or worse, the answer itself doesn’t match the source at all.”
- “My sources are unreliable because my documents are in various languages, and so is the AI’s output.”
These issues become even more problematic in complex agentic workflows, where an AI agent performs multiple tasks across long contexts and a large-scale of LLM inferences — making it increasingly difficult to consistently trace every answer back to precise, document-level evidence.
Bringing a more comprehensive citation mechanism with a seamless user experience that allows users to fact-check LLM answers straightforwardly, including in agentic workflows, has become necessary, in particular in finance and legal applications, where users deal with important volumes of data and time-pressure.
Presenting Lampi’s multi-layered citation system
That’s why at Lampi AI, we’ve been working on a way to integrate rich, document-level citations directly into our agentic system, to enhance trustworthy and verifiable AI.
Agentic applications (i.e., system orchestrating AI workflows) must be transparent, traceable, and trustworthy. When LLMs act as researchers, assistants, or analysts, we must ask not just intelligent responses — but verifiable ones.
Our state-of-the-art citation mechanisms – coming from multiple iterations and work experimenting with multiple architectures – include several systems, balancing performance, latency, and accuracy, that work together to ensure every answer we generate is grounded in traceable, document-level evidence, and generate in-line citations in every AI answer, including agentic workflows. It brings end-to-end trustworthiness through granular citations, with the right source content tied to every insight,
No matter the model you choose — Deepseek, Qwen 2.5, Mistral, Claude, GPT 4.o Gemini, or others — our citation system consistently outperforms every other approach in the market. It delivers unmatched quality in retrieval and grounding, across models and use cases, beating the best citation/source systems you’ll come across — alongside a seamless, production-ready experience.
Our multi-layered citation mechanisms, that bring together the best of both inline and post-hoc approaches, allows a seamless UX experience, include:
- Relevant sentence-level and in-line citations for each insight and data from external data (including paragraph chaining and citations dynamically linked to each contributing paragraph),
- Post-processing steps that analyze the generated text and match it with the source data to ensure that the references displayed in the final answer are accurate and contextually relevant,
- Interactive UI element for each citation that displays the exact passage used and highlights the most relevant sentences or data,
- One-click access to the relevant document — down to the exact page, paragraph, or sentence cited. Whether it's a PDF, PPT, Docx or a scanned document, users can visually confirm the source with no extra work and navigate quickly between citations in files.
Our system supports citation mapping across multiple languages — meaning you can ask a question in English and receive citations pointing to documents in French or other languages.
Toward more Transparent and Verifiable AI
Rich citations go beyond simple references, it’s about creating intuitive interfaces that help users verify information and build trust. Our updated approach is a step forward in making AI systems more transparent and user-friendly.
With AI’s hallucination problem not going away any time soon, citations will continue to play a major role in deploying AI use cases and AI adoption.
Ready to transform how you work with AI? Contact us to request a demo.