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?

Without context, AI is a blank slate.
It doesn't know your role, experience, the technologies you work with, or even what language and what tone 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 your preferences for response format and tone.

Without context, AI responds "in general." With context — exactly as you need.

Why is this important?

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.

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 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. Expertise level

Controls the depth and tone of explanations — from simple and visual to nuanced and detail-rich.

Junior — simple words, base terminology, analogies. Fits early-career interviews.
Middle — standard depth, regular technical vocabulary, key details without overload.
Senior — nuances, trade-offs, edge cases, references to internals. Sounds like an experienced engineer.

The difference, by example. Question: "What is transaction atomicity?"

Junior: "Atomicity is all-or-nothing. Either the entire transaction completes, or the whole thing rolls back — no half-states."
Senior: "Atomicity is implemented via the write-ahead log: changes go to the WAL before commit, and if the server crashes the database either replays or rolls back the transaction from the log. An important nuance — atomicity itself doesn't guarantee visibility to other transactions, that's isolation."

Same question — different level. Pick the one that fits the interview.

3. 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.

The resume is used only when the question is about experience, projects, or decisions. For pure technical questions about theory, the AI doesn't drop "as in my Go project" into every definition of a hash map.

4. 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.

5. Response language

Choose the response language — Russian, English, or another.
Useful if the interview is in English and you want a translated version.

6. Answer format and length

Configure the structure: definition + example, STAR (Situation — Task — Action — Result), Problem-Action-Result, or your own template. And separately — the target length: short, medium, or extended.

7. "Answer in my voice" mode

A true spoken-speech mode: the answer is a ready-to-read script you can voice to the interviewer, without bookish phrasings like "is defined as" or "represents," without bullet lists in conceptual answers.

Inside this mode — two fine-tuning settings:

Speaking pace — slow (long sentences, to stall for time and think aloud), normal, or fast (short, sharp phrases, no fluff).
Conversational words — none (like a lecture), some ("basically", "essentially") or natural ("well", "you know", "basically").

Example of "Senior + Answer in my voice + natural conversational words":

Well, atomicity is basically the guarantee that a transaction either fully goes through or fully rolls back.
Under the hood it's a write-ahead log: everything is written to the journal before commit, and if the server crashes — the database replays or rolls back the changes itself.
Here's an important nuance: atomicity isn't about visibility, that's isolation. People mix them up a lot.

Sounds like a real person at the interview, not like a recited article.

When this mode is on, the format and length settings are automatically hidden — they're not needed for spoken speech, everything is calibrated through pace and conversational tone.

8. Additional data

A free-form field for fine-tuning: any rules not covered by the standard options.

Examples:

Always mention specific projects from my resume
Use simple words instead of technical terms
Add pauses for natural reading

Multiple profiles

Create several contexts:

Backend Developer (Senior) — for technical interviews at a strong position
Fullstack (Middle) — for fullstack
Team Lead — for leadership positions, with "Answer in my voice" enabled

Switch between them before the interview.

How context is applied

All settings actually apply — priorities between fields are explicit, nothing is ignored "if possible."
Notes are used as your phrasing on a topic, not as universal truth for related questions.
Speech recognition errors are fixed silently. If the interviewer says "ACID" and recognition hears "ATID," the AI understands it's about transaction properties and answers correctly.
Screenshots with tasks are solved immediately — with ready-to-use code and explanation, without clarifying questions at the end.

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.
Pick the expertise level that fits the role.
Senior nuances at a junior interview will sound unnatural. Junior explanations at a senior interview will give you away.
Enable "Answer in my voice" if you plan to read answers aloud.
Without it, answers will be informative but written. With it — ready to be spoken.
Create multiple contexts.
For different interview types — 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, level, and speech style.

Five minutes of setup = quality answers = successful interview.

Good luck!