Imagine a situation: you're interviewing for a Python developer position, and the AI starts talking about Java.
Or you mention your React experience, and the assistant responds as if you've never worked with it.
Sound familiar?
The problem is that without context, AI is a blank slate.
It doesn't know your role, experience, technologies you work with, or even what language you want responses in.
Contexts solve this problem.
What is context in simple terms?
Context is your "memory" for AI.
It's a setting where you tell the assistant about yourself: your role, experience from your resume, technologies you know, and even preferences for response format.
Without context, AI responds "in general." With context — exactly as you need.
Why is this important?
The problem without context
When you ask AI a question without configured context, it responds abstractly. For example:
You: Tell me about your database experience
AI without context:
Databases are data storage systems.
There are relational databases like PostgreSQL and MySQL, NoSQL like MongoDB...
The answer is correct, but too general. It doesn't account for your experience, doesn't mention your specific projects, and isn't adapted to your role.
Solution with context
Now imagine you've configured context:
set the role Senior Backend Developer, added your resume with PostgreSQL and Redis experience, specified technologies Python, FastAPI, Docker.
The same question now gets a completely different answer:
AI with context:
In project X, we used PostgreSQL for transactional data storage and Redis for caching.
Set up replication via streaming replication, which allowed handling loads up to 10,000 RPS...
The difference is obvious: the answer accounts for your experience, mentions specific technologies, and sounds like a real case.
What can you configure in context?
1. Role or position
Specify the position you're applying for: Frontend Developer, DevOps Engineer, Data Scientist — and AI will adapt responses to that role.
Example:
If Frontend Developer is specified, the assistant will focus on React, TypeScript, and UI/UX, not server technologies.
2. Your resume
Upload your resume text or paste it manually.
This gives AI a complete picture of your experience.
How it works:
When the interviewer asks "Tell me about project X," AI already knows what you did there and responds based on your real experience.
3. Technologies
List technologies you work with: Python, React, Docker, Kubernetes, PostgreSQL, etc. AI will use exactly these in responses.
Example:
If Python, FastAPI, PostgreSQL is specified, the assistant won't mention Java or MongoDB.
4. Response language
Choose the response language — Russian, English, or another.
Useful if the interview is in English and you want a translated version.
5. Response format
Configure response style: short, detailed, with code or without.
Example:
"Give short answers to simple questions (1-2 sentences) and detailed ones with code examples for technical questions."
6. Additional data
Add additional data:
- Always mention specific projects from your resume
- Use simple words instead of technical terms
- Add pauses for natural reading
Multiple profiles
Create several contexts:
Backend Developer— for backend interviewsFullstack Developer— for fullstackTeam Lead— for leadership positions
Switch between them before the interview.
How to use contexts correctly?
-
Configure context before the interview.
Spend 5 minutes — and get relevant answers. -
Upload an up-to-date resume.
The more accurate the data, the better the response quality. -
Specify real technologies.
Don't add things you don't know. -
Use additional data.
This is fine-tuning that makes responses natural. -
Create multiple contexts.
For different types of interviews — different profiles.
Conclusion
Contexts are not a formality, but the foundation of personalization.
They turn dry AI responses into meaningful ones tied to your experience.
Five minutes of setup = quality answers = successful interview.
Good luck!