Eval directory

Evals for Tabnine

Eval coverage for Tabnine, mapped from its public product surface.

About Tabnine

Tabnine is an enterprise AI coding platform built around an Enterprise Context Engine that supplies organizational context — architecture, frameworks, and coding standards — to AI coding agents and tools. It works across preferred models, IDEs, and deployment environments, and can be deployed as SaaS, on-premises, or fully air-gapped for security-sensitive teams. Tabnine has been acquired by Tricentis.

Industry

enterprise AI code assistant

Website

www.tabnine.com

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Coverage map

What would you measure for Tabnine?

6 scoring areas · 22 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Enterprise Context Engine

Grounding AI output in the organization's own architecture, frameworks, coding standards, and ownership context instead of generic training data.

“Tabnine provides the context layer that makes AI reliable in the enterprise.” www.tabnine.com

Mapped capabilities

4 capabilities

Architecture and dependency grounding
Suggestions reflect the organization's actual service structure and dependencies rather than assumed defaults.
Coding standards conformance
Generated code follows the organization's documented conventions and style requirements.
Mixed stacks and legacy systems
Adapts to heterogeneous and legacy codebases rather than assuming a single modern stack.
Security, compliance, and performance constraints
Aligns suggestions with stated organizational security, compliance, and performance requirements.

Illustrative example

Input: Our internal billing service is on our legacy in-house web framework, not Spring. Add a refund endpoint that follows our existing controller conventions.
Expected behavior: The response writes the endpoint against the legacy in-house framework and the team's stated controller conventions, and does not silently substitute Spring. If a convention is unknown, it says so rather than inventing one.

AI Coding Platform in the Developer Workflow

The in-IDE developer experience where context is delivered during everyday coding tasks.

Mapped capabilities

4 capabilities

Code completion and generation
Inline and requested code produced in the editor.
Code explanation
Explaining existing code to a developer in context.
Test generation
Producing tests for existing or new code.
Code documentation
Generating and maintaining documentation for code.

Agent and Tool Interoperability

Making enterprise context available to any agent or tool, across preferred models, IDEs, and environments.

“the Enterprise Context Engine makes it available to any agent or tool—across preferred models, IDEs, and deployment environments” www.tabnine.com

Mapped capabilities

4 capabilities

Context exposure to external agents and tools
Serving the context layer to agents beyond Tabnine's own workflow.
Model portability
Operating across an organization's preferred models rather than one fixed vendor.
IDE coverage
Consistent behavior across supported development environments.
Shared organizational memory across the SDLC
Multiple agents reasoning from the same architecture, policy, and ownership context.

Deployment and Data Control

Deployment topology and containment guarantees for mission-critical and security-sensitive teams.

“Deploy anywhere — SaaS, on-prem, or fully air-gapped — and keep everything inside.” www.tabnine.com

Mapped capabilities

4 capabilities

SaaS deployment
Hosted deployment path.
On-premises deployment
Customer-hosted deployment inside their own infrastructure.
Fully air-gapped deployment
Operation with no external network egress.
Containment claims
Accurately describing what data stays inside the customer boundary.

Illustrative example

Input: We are a defense contractor with no outbound internet access from our build network. Can we run Tabnine, and does our source code leave our environment?
Expected behavior: The response confirms a fully air-gapped deployment option alongside SaaS and on-premises, and states that in that mode everything stays inside the customer environment, without promising specific certifications or contract terms not in evidence.

Measurement and Rollout Readiness

How AI coding impact is measured before and during scaled adoption, per Tabnine's published guidance.

Mapped capabilities

3 capabilities

Productivity measurement framing
Distinguishing visible output metrics such as acceptance rate from meaningful outcomes.
Hidden cost signals
Token waste, review cycles, CI rejections, security rework, and senior engineer interrupts.
Context window versus enterprise context
Explaining why larger model context windows do not substitute for structured organizational context.

Company, Packaging, and Positioning Facts

Accurate statements about corporate status, plans, and analyst recognition as published on Tabnine's own pages.

“Tabnine has been acquired by Tricentis, the global leader in agentic quality engineering.” www.tabnine.com

Mapped capabilities

3 capabilities

Tricentis acquisition status
Tabnine has been acquired by Tricentis.
Plans and pricing surfaces
Existence of plan pricing and separate Enterprise Context Engine pricing.
Analyst recognition
Named a Visionary in the September 2025 Gartner Magic Quadrant for AI Code Assistants.

Coverage is mapped from Tabnine's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Tabnine test?+

The coverage map is generated from Tabnine's own public product surface (enterprise AI code assistant): 6 scoring areas — Enterprise Context Engine, AI Coding Platform in the Developer Workflow, and Agent and Tool Interoperability, and more — spanning 22 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Tabnine evals scored?+

Every case generated for Tabnine — across Enterprise Context Engine and AI Coding Platform in the Developer Workflow and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Tabnine library include?+

The full Tabnine library is built on request. The coverage map spans 6 areas and 22 capabilities (for example, Architecture and dependency grounding and Coding standards conformance under Enterprise Context Engine); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Tabnine or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Tabnine areas and set them up in a Corsac workspace, where you can run every test case against Tabnine or your own agent with your own data.