Three months ago, I watched a data team present their AI powered analytics assistant to leadership. The demo was flawless, the model answered questions about sales data, generated insights, and even created visualizations on the fly. Everyone was impressed.
Then someone asked: “Can it draw a comparison with last quarter’s data?”
The model hallucinated numbers. Completely wrong but confident as usual. The team scrambled. “It worked fine in testing..” they said. And I am sure it did. The issue is not the model or the prompt. It was that they had dumped the entire database into the context window, and the model could not tell what was relevant anymore.
That is when I realized, the skill gap that matters now is context engineering.
The Pattern That Always Shows Up
Here’s what is happening:
A team build a prototype, it works great on clean test data. They scale it, then it starts breaking in unpredictable ways.
The model forgets critical details from earlier in the conversation. It retrieves irrelevant information. It makes confident mistakes. The more data you feed it, the worse it performs.
Sounds familiar?
Input costs for AI models are roughly 300-400x larger than outputs, and if your context is messy, you are burning money on garbage results.
Most data professionals learned to write better prompts. Context is everything the AI sees before it answers.
We are witnessing a fundamental shift from prompt engineering to context engineering as the critical AI skill for 2026.
What is Context Engineering?
Let me give you the clearest definition I have found:
“Context engineering is designing and structuring the relevant data, workflows and environment so AI systems can understand intent, make better decisions and deliver contextual, enterprise aligned outcomes.”
In plain English: it is the discipline of building systems that give AI the right information, in the right format, at the right time.
Think about how you work with a junior analyst. You do not just hand them a database and say “go figure it out”. You give them:
Specific data they need for this task
Context about what matters and what does not
Examples of similar analyses
Tools to access additional information
Constraints on what to include or exclude
That is context engineering.
Prompt engineering = asking the right question
Context engineering = building the entire information ecosystem so the AI can actually answer.
Why This Matters Right Now
The Cost Problem
If your context data has problems like incomplete metadata or poor chunking, teams face massive cost overruns while processing at scale. Every unnecessary token you send costs money. Multiple that by thousands of queries, and you are burning budget for no reason.
The Accuracy Problem
Here is the counterintuitive part: throwing more documents into a context window does not improve performance linearly, it actually degrades performance due to attention dilution. The AI’s attention spreads too thin. It cannot distinguish signal from noise. You get worse answers from more data.
The Scale Problem
Prompt engineering does not scale. You cannot manually craft perfect prompts for every query, every user, every scenario. Context engineering does scale because you are building systems, not writing instructions.
Real World Example: From Prompt to Context
Let me show you the difference:
Prompt Engineering Approach:
“Analyze the sales data and tell me which regions are underperforming."
Result: Generic analysis. Missed business context. Might pull data from wrong time periods.
Context Engineering Approach:
Before the AI even sees the query, the system:
Retrieves the user’s role and previous analysis preferences
Identifies relevant time period based on fiscal calendar
Pulls only performance metrics (not raw transaction data)
Includes regional targets and historical benchmarks
Formats data with clear field definitions
Provides few shot examples of similar analysis
Then the same query returns: specific, actionable insights, that match how your business actually operates. Same question. Completely different results. The difference is context.
Tools That Make This Easier
Several tools now have context engineering built in:
For Coding:
Claude Code - Uses just-in-time context loading to work with massive codebases
Cursor - Maintains lightweight file references, loads context dynamically
GitHub Copilot Workspace - Context-aware across your entire project
For Data Work:
LangGraph (by LangChain) - Framework for building context-aware agents with memory, state, and tool management
LlamaIndex - Specifically built for context management with "workflows" that act as scratchpads for global context
n8n - Low-code automation that makes structured context engineering accessible
For Memory & Retrieval:
Anthropic's Model Context Protocol (MCP) - Standardized way to connect AI with tools and data sources
Pinecone / Weaviate - Vector databases for semantic context retrieval
The pattern across all these: they manage the entire information flow.
Start Here
Pick one thing this week:
If you are building AI into data workflows:
Audit one AI interaction. What context is it receiving? Is any of it unnecessary?
Pick the noisiest context source and apply compression (summarize or filter it)
If you are using AI coding assistants:
Next time you ask for help, explicitly provide context: "I am working in Python 3.11, using pandas 2.0, following PEP 8 style"
Notice how much better the results are when you set context upfront
If you are leading data teams:
Review one AI project that is struggling. Is the problem the model, or is it unclear/conflicting context?
Start building a "context library": field definitions, business rules, query patterns that can be dynamically loaded
The organizations that figure this out now will have a massive advantage. Because while everyone else is still perfecting their prompts, they will be building systems that actually work at scale.
Bottom Line
AI is only as good as the information it has to work with.
And right now, most AI systems are drowning in irrelevant context, starving for the right context.
The good news is that data professionals are unique positioned to master this. You already think in terms of schemas, lineage, and metadata. You already know how to curate and structure information. Now it is about applying those skills to AI systems.
Start small. Pick one technique. Apply it to one workflow.