The hottest discussion in AI right now, at least the one not about
Agentic AI, is about how “context engineering” is more important than prompt engineering, how you give AI the data and information it needs to make decisions, and it cannot (and must not) be a solely technical function.
“‘Context’ is actually how your company operates; the ideal versions of your reports, documents & processes that the AI can use as a model; the tone & voice of your organization. It is a cross-functional problem.”
So says renowned Tech Influencer and Associate Professor at Wharton School, Ethan Molick.
He in turn cites fellow Tech Influencer Andrej Karpathy on X, who in turn cites Tobi Lutke, CEO of Shopify:
“It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM. ”
The three together – Molick, Karpathy and Lutke – make for a powerful triumvirate of Tech-influencers.
Karpathy consolidates the subject nicely. He emphasizes that in real-world, industrial-strength LLM applications, the challenge entails filling the model’s context window with just the right mix of information.
He thinks about context engineering as both a science—because it involves structured systems and system-level thinking, data pipelines, and optimization —and an art, because it requires intuition
about how LLMs interpret and prioritize information. His analysis reflects two of my predictions for 2025 one highlighting the increasing
impact of uncertainty and another a growing appreciation of knowledge.
Tech mortals offered further useful comments on the threads, two of my favorites being:
“Owning knowledge no longer sets anyone apart; what matters is pattern literacy—the ability to frame a goal, spot exactly what you don’t know, and pull in just the right strands of information while
an AI loom weaves those strands into coherent solutions.” “It also feels like ‘leadership’ Tobi. How to give enough information, goal and then empower.”
I love the AI loom analogy, in part because it corresponds with one of my favorite data descriptors, the “Contextual
Fabric”. I like the leadership positivity too, because the AI looms and contextual fabrics, are led by and empowered by humanity.
Here’s my spin, to take or leave. Knowledge, based on data, isn’t singular, it’s contingent, contextual. Knowledge and thus the contextual fabric of data on which it is embedded is ever changing, constantly shifting, dependent on situations and needs. I
believe knowledge is shaped by who speaks, who listens, and what about. That is, to a large extent, led by power and the powerful. Whether in Latin, science, religious education, finance and now AI, what counts as “truth” is often a function of who gets to
tell the story. It’s not just about what you know, but how, why, and where you know it, and who told you it.
But of course it’s not that simple; agency matters – the peasant can become an abbot, the council house schoolgirl can become a Nobel prize-winning scientist, a frontier barbarian can become a Roman emperor. For AI, truth to power is held by the big tech
firms and grounded on bias, but on the other it’s democratizing in that all of us and our experiences help train and ground AI, in theory at least.
I digress. For AI-informed decision intelligence, context will likely be the new computation that makes GenAI tooling more useful than simply being an oft-hallucinating stochastic parrot, while enhancing traditional AI – predictive machine learning, for
example – to be increasingly relevant and affordable for the enterprise.
Context Engineering for FinTech
Context engineering—the art of shaping the data, metadata, and relationships that feed AI—may become the most critical discipline in tech.
This is like gold for those of us in the FinTech data engineering space, because we’re the dudes helping you create your own context.
I’ll explore how five different contextual approaches, all representing data engineering-relevant vendors I have worked for —technical computing, vector-based, time-series, graph and geospatial platforms—can support context engineering.
Parameterizing with Technical Computing
Technical computing tools – think R, Julia, MATLAB and Python’s SciPy stack – can integrate domain-specific data directly into the model’s environment through structured inputs, simulations, and real-time sensor data, normally as
vectors, tables or matrices. For example, in engineering or robotics applications, an AI model can be fed with contextual information such as system dynamics, environmental parameters, or control constraints. Thus the model can make decisions that are
not just statistically sound but also physically meaningful within the modeled system.
They can dynamically update the context window of an AI model, for example in scenarios like predictive maintenance or adaptive control, where AI must continuously adapt to new data. By embedding contextual cues, like historical trends, operational thresholds,
or user-defined rules, such tools help ground the model’s outputs in the specific realities of the task or domain.
Financial Services Use Cases
Quantitative Strategy Simulation
Simulate trading strategies and feed results into an LLM for interpretation or optimization.
Stress Testing Financial Models
Run Monte Carlo simulations or scenario analyses and use the outputs to inform LLMs about potential systemic risks.
Vectors and the Semantics of Similarity
Vector embeddings are closely related to the linear algebra of technical computing, but they bring semantic context to the table. Typically stored in so-called vector databases, they encode meaning into high-dimensional space, allowing AI to retrieve
through search not just exact matches, but conceptual neighbors. They thus allow for multiple stochastically arranged answers, not just one.
Until recently,
vector embeddings and vector databases have been primary providers of enterprise context to LLMs, shoehorning all types of data as searchable mathematical vectors. Their downside is their brute force and compute-intensive approach to storing and searching
data. That said, they use similar transfer learning approaches – and deep neural nets – to those that drive LLMs. As expensive, powerful brute force vehicles of Retrieval-Augmented
Generation (RAG), vector databases don’t simply just store documents but understand them, and have an increasingly proven place for enabling LLMs to ground their outputs in relevant, contextualized knowledge.
Financial Services Use Cases
Customer Support Automation
Retrieve similar past queries, regulatory documents, or product FAQs to inform LLM responses in real-time.
Fraud Pattern Matching
Embed transaction descriptions and retrieve similar fraud cases to help the model assess risk or flag suspicious behavior.
Time-Series, Temporal and Streaming Context
Time-series database and analytics providers, and in-memory and columnar databases that can organize their data structures by time, specialize in knowing about the when. They can ensure temporal context—the heartbeat of many use cases in financial markets
as well as IoT, and edge computing- grounds AI at the right time with time-denominated sequential accuracy. Streaming systems, like Kafka, Flink, et al can also facilitate the real-time central nervous systems of financial event-based systems.
It’s not just about having access to time-stamped data, but analyzing it in motion, enabling AI to detect patterns, anomalies, and causality, as close as possible to real time. In context engineering, this is gold. Whether it’s fraud that happens in milliseconds
or sensor data populating insurance telematics, temporal granularity can be the difference between insight and noise, with context stored and delivered by what some might see as
a data timehouse.
Financial Services Use Cases
Market Anomaly Detection
Injecting real-time price, volume, and volatility data into an LLM’s context allows it to detect and explain unusual market behavior.
High-Frequency Trading Insights
Feed LLMs with microsecond-level trade data to analyze execution quality or latency arbitrage.
Graphs That Know Who’s Who
Graph and relationship-focussed providers play a powerful role in context engineering by structuring and surfacing relationships between entities that are otherwise hidden in raw data. In the context of
large language models (LLMs), graph platforms can dynamically populate the model’s context window with relevant, interconnected knowledge—such as relationships between people, organizations, events, or transactions. They enable the model to reason more
effectively, disambiguate entities, and generate responses that are grounded in a rich, structured understanding of the domain. Graphs can act as a contextual memory layer through
GraphRAG and Contextual RAG, ensuring that the LLM operates with awareness of the most relevant and trustworthy information.
For example, graph databases – or other environments, e.g. Spark, that can store graph data types as accessible files, e.g. Parquet, HDFS – can be used to retrieve a subgraph of relevant nodes and edges based on a user query, which can then be serialized
into natural language or structured prompts for the LLM.
Platforms that focus graph context around entity resolution and contextual decision intelligence can enrich the model’s context with high-confidence, real-world connections—especially useful in domains like fraud detection, anti-money laundering, or customer
intelligence. Think of them as like Shakespeare’s Comedy of Errors meets Netflix’s
Department Q. Two Antipholuses and two Dromios rather than 1 of each in Comedy of Errors? Only 1 Jennings brother to investigate in Department Q’s case, and where does Kelly MacDonald fit into anything? Entity resolution and graph context can help
resolve and connect them in a way that more standard data repositories and analytics tools struggle with. LLMs cannot function without correct and contingent knowledge of people, places, things and the relationships between them, though to be sure many types
of AI can also help discover the connections and resolve entities in the first place.
Financial Services Use Cases
AML and KYC Investigations
Surface hidden connections between accounts, transactions, and entities to inform LLMs during risk assessments.
Credit Risk Analysis
Use relationship graphs to understand borrower affiliations, guarantors, and exposure networks.
Seeing the World in Geospatial Layers
Geospatial platforms support context engineering by embedding spatial awareness into AI systems, enabling them to reason about location, proximity, movement, and environmental context. They can provide rich, structured data layers (e.g., terrain, infrastructure,
demographics, weather) that can be dynamically retrieved and injected into an LLM’s context window. This allows the model to generate responses that are not only linguistically coherent but also geographically grounded.
For example, in disaster response, a geospatial platform can provide real-time satellite imagery, flood zones, and population density maps. This data can be translated into structured prompts or visual inputs for an AI model tasked with coordinating relief
efforts or summarizing risk. Similarly, in urban planning or logistics, geospatial context helps the model understand constraints like traffic patterns, zoning laws, or accessibility. In essence, geospatial platforms act as a spatial memory layer, enriching
the model’s understanding of the physical world and enabling
more accurate, context-aware decision-making.
Financial Services Use Cases
Branch Network Optimization
Combine demographic, economic, and competitor data to help LLMs recommend new branch locations.
Climate Risk Assessment
Integrate flood zones, wildfire risk, or urban heat maps to evaluate the environmental exposure of mortgage and insurance portfolios.
Context Engineering Beyond the Limits of Data, Knowledge & Truths
Context engineering I believe recognizes that data is partial, and that knowledge and perhaps truth or truths needs to be situated, connected, and interpreted. Whether through graphs, time-series, vectors, tech computing platforms, or geospatial layering,
AI depends on weaving the right contextual strands together.
Where AI represents the loom, the five types of platforms I describe are like the spindles, needles, and dyes drawing on their respective contextual fabrics of ever changing data, driving threads of knowledge—contingent, contextual, and ready for action.
