Beyond Search: How Prompt Engineering is Reshaping Financial Data Retrieval

Author: Denis Avetisyan

A new framework, PRISM, leverages the power of carefully crafted prompts and in-context learning to significantly improve how we access and analyze financial information.

The proposed PRISM framework offers a novel approach to a problem, acknowledging that even the most innovative solutions inevitably contribute to future technical debt as production environments expose unforeseen limitations.

PRISM utilizes a multi-agent system with prompt refinement to enhance document and chunk ranking for financial information retrieval, achieving strong results on the FinAgentBench dataset.

Extracting actionable intelligence from extensive financial documents remains challenging despite advances in large language models. This paper introduces ‘PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval’, a training-free framework designed to enhance financial information retrieval through synergistic prompt engineering, in-context learning, and a lightweight multi-agent system. Experiments on the FinAgentBench dataset demonstrate PRISM’s ability to effectively rank both documents and chunks, achieving a compelling NDCG@5 of 0.71818. Could this modular, inference-only approach represent a practical pathway toward scalable and robust financial analysis solutions?

The Illusion of Insight: Why Financial Data Still Hides its Secrets

Conventional approaches to financial document retrieval frequently fall short due to the inherent complexity and subtlety within these texts. Systems reliant on keyword matching or simple statistical analysis often fail to capture the nuanced relationships between concepts, missing critical insights hidden within lengthy reports, regulatory filings, and market analyses. Financial language is replete with jargon, implicit assumptions, and context-dependent meanings, presenting a significant challenge for algorithms designed to interpret information literally. Consequently, vital data points – such as subtle risk indicators, emerging trends, or crucial caveats – can be overlooked, leading to incomplete or inaccurate assessments. The limitations of these traditional methods highlight the need for more advanced techniques capable of understanding the semantic meaning and contextual relevance of financial information, ultimately improving the reliability and efficiency of financial decision-making.

The relentless surge in financial data-reports, news articles, regulatory filings, and alternative datasets-presents a formidable challenge to traditional information retrieval systems. These systems, often reliant on keyword searches and rule-based approaches, struggle to keep pace with both the volume and the complexity of modern financial information. Consequently, sophisticated techniques-including natural language processing, machine learning, and knowledge graph construction-are increasingly necessary to efficiently extract, analyze, and interpret data. The ability to quickly and accurately access critical insights within this deluge is no longer simply a matter of convenience, but a fundamental requirement for informed decision-making, risk management, and maintaining a competitive edge in the financial landscape.

PRISM: A Framework Built on Sand (and Some Clever Engineering)

PRISM utilizes a combined approach to financial information retrieval by integrating System Prompt Engineering, In-Context Learning (ICL), and a Multi-Agent System (MAS). System Prompt Engineering establishes clear instructions for the language model, guiding its responses and ensuring relevance to financial queries. ICL enhances performance by providing the model with illustrative examples directly within the prompt, enabling it to generalize to unseen data. Finally, a MAS distributes the retrieval task across multiple agents, each specializing in a particular aspect of information assessment, and aggregates their results to produce a more robust and reliable ranking of relevant financial documents. This synergistic combination aims to overcome the limitations of traditional keyword-based search and improve the accuracy and efficiency of financial data access.

In-Context Learning (ICL) within PRISM utilizes Text Embedding Models to convert financial text data into high-dimensional vector representations, capturing semantic meaning. These vectors are then stored and efficiently indexed using a FAISS Vector Store, enabling rapid similarity searches based on vector distance. This approach allows PRISM to retrieve information not based on keyword matches, but on contextual relevance, identifying documents with similar meanings even if they use different terminology. The combination of Text Embedding Models and FAISS facilitates scalable and accurate semantic search, improving the system’s ability to understand and retrieve nuanced financial information.

The Multi-Agent System (MAS) within PRISM employs a collaborative architecture wherein multiple specialized agents independently rank retrieved financial documents based on relevance to the query. Each agent utilizes distinct ranking criteria and algorithms, and a consensus mechanism aggregates these individual rankings to produce a final, consolidated result. This approach mitigates the risk of bias inherent in single-model ranking systems and improves overall reliability by leveraging the diversity of ranking strategies. Discrepancies in agent rankings are resolved through a weighted voting scheme, prioritizing agents with demonstrated higher historical accuracy, thereby enhancing the robustness and trustworthiness of the final ranked list.

Prompting the Beast: A Delicate Dance with LLMs

System Prompt Engineering serves as the core methodology within the PRISM framework for augmenting Large Language Model (LLM) performance. This involves crafting specific, detailed prompts that guide the LLM’s reasoning process. Techniques built upon this foundation include Chain-of-Thought (CoT) Prompting, which encourages step-by-step reasoning; ReAct Prompting, integrating reasoning with acting and observation; and Tree-of-Thoughts (ToT) Prompting, exploring multiple reasoning paths. These prompting strategies effectively extend the capabilities of LLMs, such as GPT-4 and GPT-5, beyond simple text completion by enabling more complex problem-solving and decision-making processes.

Advanced prompting strategies, such as Chain-of-Thought, ReAct, and Tree-of-Thoughts, facilitate structured reasoning within Large Language Models (LLMs) like GPT-4 and GPT-5 by decomposing complex problems into intermediate steps. These techniques move beyond simple input-output mappings, encouraging the LLM to articulate its thought process – generating a sequence of reasoning steps before arriving at a final answer. This staged approach improves accuracy and allows for error analysis, as each step can be evaluated for logical consistency and factual correctness. The models are effectively guided to simulate a more deliberate and traceable reasoning pathway, enhancing performance on tasks requiring multi-step inference or problem-solving.

Token embedding is a crucial component of In-Context Learning (ICL) as it transforms textual data into numerical vectors, or tokens, allowing for quantifiable semantic comparison. This process utilizes algorithms to map each word or sub-word unit to a high-dimensional vector space, where the spatial relationship between vectors reflects the semantic similarity of the corresponding text. By representing text numerically, LLMs can assess the relevance of input examples and effectively utilize provided context during inference. The resulting token embeddings enable the model to identify patterns and relationships within the data that would be impossible with purely symbolic representations, thereby enhancing the accuracy and efficiency of ICL.

Performance: Close Enough for Government Work (and Financial Institutions)

Evaluations using the FinAgentBench dataset reveal that PRISM significantly enhances ranking accuracy, achieving a Normalized Discounted Cumulative Gain at 5 (NDCG@5) score of 0.71163. This metric, widely used in information retrieval, assesses the ranking quality by assigning higher weights to relevant items appearing earlier in the ranked list; a score of 0.71163 indicates a strong ability to prioritize pertinent financial agent responses. The improvement suggests that PRISM effectively discerns and ranks more relevant information, offering a notable advancement in the performance of financial agent systems and demonstrating its capacity to deliver more useful and accurate results to users.

Rigorous evaluation of PRISM reveals a performance level remarkably close to that of current state-of-the-art models in the field. Testing on a private validation set demonstrated an average performance gap of just 0.006, indicating PRISM’s strong competitive edge and ability to achieve results on par with leading systems. This narrow margin suggests that PRISM effectively captures the nuances of complex financial agent interactions, showcasing its potential as a highly effective framework for financial reasoning tasks and providing a compelling alternative to existing solutions.

Rigorous statistical analysis substantiates the observed performance gains of PRISM relative to established baseline models. Utilizing p-values consistently below the significance threshold of 0.05, researchers established that the improvements achieved are unlikely due to random chance. This stringent statistical validation strengthens the claim that PRISM represents a genuine advancement in the field, offering a reliable and demonstrable improvement over existing methods. The consistently low p-values provide confidence in the robustness of the findings and suggest that PRISM’s enhanced performance is a consistent and repeatable phenomenon, rather than a fleeting result of specific data or conditions.

Consistent performance is a hallmark of a robust artificial intelligence framework, and PRISM delivers on this front through demonstrated stability and reproducibility. Evaluations reveal a Coefficient of Variation (CV) of less than 1.6% across multiple experimental runs, indicating that the framework yields remarkably similar results each time it is executed. This low $CV$ score signifies minimal variance in outcomes, bolstering confidence in the reliability of PRISM’s analyses and predictions. Such consistency is crucial for practical applications, ensuring that observed performance isn’t simply due to chance and that the framework can be consistently relied upon for accurate and dependable results.

The top ten teams were ranked based on their performance on the private test dataset.

Beyond Finance: The Inevitable Expansion (and the Limits of Generalization)

The PRISM framework, initially developed for financial analysis, possesses a highly adaptable architecture poised for impactful application in domains demanding sophisticated reasoning and information retrieval. Its core principles – the structured decomposition of complex queries, the parallel processing of diverse data sources, and the synthesis of evidence-based conclusions – translate seamlessly to fields like legal discovery and medical diagnosis. In law, PRISM could expedite the review of vast document collections, identifying relevant precedents and arguments with greater efficiency. Similarly, in medicine, the framework could assist clinicians by integrating patient history, genomic data, and current research to support more informed and personalized treatment plans. This versatility stems from PRISM’s domain-agnostic design, allowing it to be readily customized with new knowledge sources and inference rules, thereby unlocking its potential across a broad spectrum of complex problem-solving scenarios.

The potential of PRISM is significantly amplified when considered alongside advancements in large language models and knowledge graphs. Future studies could investigate synergistic combinations, leveraging the reasoning capabilities of PRISM with the expansive knowledge and natural language processing strengths of more sophisticated LLMs. Integrating PRISM with structured knowledge graphs would allow for even deeper contextual understanding and validation of information, moving beyond textual analysis to incorporate factual relationships and domain-specific expertise. This convergence promises a system capable of not just retrieving information, but synthesizing it into nuanced insights, effectively bridging the gap between data and actionable intelligence, and potentially unlocking novel approaches to complex problem-solving across diverse fields.

The PRISM framework offers a pathway to fundamentally reshape practices within the financial sector. By effectively synthesizing vast datasets – encompassing market trends, economic indicators, and even unstructured data like news reports and social media sentiment – it enables a more nuanced and predictive approach to financial analysis. This capability extends beyond simple forecasting; PRISM facilitates the identification of previously hidden risks, allowing for proactive risk management strategies and more informed investment decisions. Ultimately, the framework’s ability to move beyond traditional, reactive methods towards a proactive, data-driven paradigm promises to unlock significant gains in efficiency, accuracy, and profitability across the entire financial landscape.

The pursuit of elegant frameworks, as demonstrated by PRISM’s multi-agent approach to financial retrieval, invariably leads to… something else. It’s a sophisticated dance of prompt engineering and in-context learning, attempting to wrangle the chaotic nature of financial data. Donald Davies observed, “The trouble with most computers is that they’re fast.” This feels profoundly applicable. PRISM, like so many before it, builds a layer of complexity atop existing complexity, hoping to achieve a semblance of order. It might rank document chunks more effectively now, but one can be reasonably certain production will discover a novel way to break it. It’s not a failure; it’s just leaving more detailed notes for future digital archaeologists.

The Road Ahead

PRISM, like all elegantly constructed frameworks, solves a known problem with admirable ingenuity. The gains on FinAgentBench are… encouraging. But the bug tracker will inevitably fill. This isn’t a question of if edge cases will break the multi-agent consensus, but when, and how spectacularly. The current reliance on prompt engineering feels less like a solution and more like a particularly verbose form of feature creep. Each refined prompt is a temporary bandage on a fundamentally opaque system.

The real challenge isn’t ranking document chunks; it’s understanding why those rankings fail. Future work will not be measured by leaderboard scores, but by the granularity of failure analysis. One anticipates a proliferation of ‘explainable AI’ layers grafted onto these models, each adding complexity in a desperate attempt to predict the unpredictable. The pursuit of ‘general’ financial reasoning remains a distant, and perhaps illusory, goal.

It is worth remembering that these systems don’t ‘retrieve’ information – they hallucinate plausible connections. The next iteration won’t be about better prompts or more agents, but about accepting the inherent stochasticity. They don’t deploy – they let go.

Original article: https://arxiv.org/pdf/2511.14130.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/