Most tools today still behave like goldfish. They react to the last click, the last prompt, the last input. Then they forget everything and start over. That works in demos. It falls apart in real workflows.
What's changing now is simple: systems are starting to understand situations, not just inputs. Context isn't a feature. It's the product.
What Contextual AI Actually Means
Most AI products today are just a language model wrapped in a UI. Contextual AI is what happens when you build a system around the model. Three things consistently show up:
- Context awareness, looking beyond the current input
- Personalization, adapting to the individual over time
- Continuous learning, accumulating patterns instead of resetting
UX research on context-aware systems keeps reinforcing the same point: real personalization comes from combining short-term context with long-term behavior.
Why Prompting Isn't the Bottleneck Anymore
Generating text is easy now. The hard part is generating the right output inside a workflow. Without context, you get:
- Forgetting
- Irrelevance
- Extra work
This isn't theoretical. We saw this immediately in Draft. Early versions would generate strong bullet points that had nothing to do with the job you were applying to. The model wasn't wrong. It just didn't know what mattered right now.
That's the core failure. Not intelligence. Missing context.
What This Looks Like in the Real World
Gmail is the cleanest example. Old Smart Reply reacted to the last message. Newer versions read the entire thread and generate responses that actually track the conversation. That shift is well documented in analyses of Gmail's contextual reply system. It's not better prompts. It's better context.
Under the Hood: How Contextual Systems Actually Work
There are four layers that matter.
1. Signals: what the system listens to
Everything starts with signals, past edits, saved jobs, resume content, time between actions. We initially underestimated this. Draft v1 mostly looked at the resume and the current job description. That sounds reasonable. It wasn't enough.
Users behave differently across sessions. They explore, compare, abandon, come back. If you don't capture that, your system resets mentally every time, and suggestions start feeling repetitive.
2. State and memory: what the system remembers
We made a classic mistake early, treating each resume edit like a stateless request. Users would reject a suggestion and then see a slightly reworded version of the same suggestion five minutes later. Nothing kills trust faster.
Fixing this meant introducing memory: tracking accepted vs rejected edits, storing job-specific context, persisting user preferences across sessions.
Frameworks like LangGraph formalize this as state graphs, but the underlying idea is simple: if your system doesn't remember decisions, it doesn't feel intelligent.
3. Context construction: what the model actually sees
This was the most counterintuitive lesson. More context doesn't mean better output. Our early instinct was to feed everything, full resume, full job description, full edit history. The output got worse. The model couldn't tell what mattered.
The fix was aggressive filtering: pull only the most relevant experience, extract key requirements from the JD, summarize past edits into constraints.
This is what people now call context engineering. And honestly, it's where most of the product quality comes from.
Contextual AI for Resume Optimization (Draft)
Once the system started working, the difference was obvious. A contextual resume system understands the job beyond keywords, maps candidate experience to real requirements, and adapts suggestions as the user iterates.
But the real unlock was persistence. If a user consistently ignored leadership-related suggestions, the system stopped pushing them. If they switched from a fintech role to a SaaS role, recommendations shifted instantly.
That's when users stopped treating it like a tool and started trusting it. And that trust came from memory, not generation quality.
Contextual AI for Recruiters
Same pattern, different side. Recruiters don't need more data. They need prioritization. But early recruiter-style systems over-optimized for scoring, everything became a number.
What we found is that raw scores without context don't help. Recruiters want to know why a candidate is a fit, what they're missing, where they might surprise. AI recruiting platforms only feel useful when grounded in full pipeline context, not isolated rankings.
Designing This Without Breaking Trust
If the system adapts but the user doesn't understand why, they assume it's wrong. Three rules fixed most of it:
- Explain decisions, not everything, just enough
- Reduce choices, too many suggestions creates paralysis
- Be transparent about memory, users should know the system is learning from them
These aren't nice-to-haves. They directly impact whether users stick or churn.
The Real Shift: Prompt Engineering → Context Engineering
Everyone starts with prompts. But prompts don't scale. The real system is:
- What you store
- What you retrieve
- What you ignore
- How you adapt over time
Frameworks like LangGraph exist because people realized this isn't about better prompts. It's about better systems.
The Bottom Line
Contextual AI isn't a feature. It's the difference between a system that responds and a system that understands.
We didn't fully get this when we started Draft. We thought better generation would solve the problem. It didn't. What actually moved the needle was memory, relevance filtering, and adaptive behavior.
Everything else was secondary. And once that clicked, the product stopped feeling like AI. It just felt useful.
Reuben Jacob — Founder of Syphon Labs, building Draft and Daisy Recruiter.
Frequently Asked Questions
What is contextual AI?
Contextual AI refers to AI systems that maintain awareness of prior inputs, user history, and task-specific state, rather than treating each interaction in isolation. Unlike a basic chatbot that forgets each message, contextual AI builds a persistent understanding of what the user is trying to accomplish. In Draft, this means the AI remembers your job description, edit history, and preferences across an entire application session.
Why do most AI tools fail to use context effectively?
Most AI tools are built on stateless LLM calls, where each prompt is independent with no memory of what came before. The result is generic output that doesn't improve with use. Building context-aware systems requires explicit state management, relevance filtering to avoid context overload, and memory that persists across multiple interactions. These are hard engineering problems, not just prompting problems.
How does Draft use context to improve resume tailoring?
Draft tracks the job description you uploaded, the edits you've accepted and rejected, and the specific role requirements throughout your session. When you ask it to revise a bullet point, it already knows the employer's language, your experience, and what kinds of changes you've already approved. This is why Draft produces better output over the course of a session rather than worse.
What is the difference between context length and contextual AI?
Context length (or context window) is simply how much text an LLM can process at once. Contextual AI is about how you use that capacity, specifically whether the system maintains relevant state, filters out noise, and builds an understanding of the user's goal over time. A long context window doesn't automatically produce contextual behavior. It requires deliberate system design around what to remember and why.
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