Why Precision Matters

When an AI coding agent answers a question like “How does server component rendering work?”, the result depends on which files are included. If most of the retrieved files are generic server utilities rather than rendering logic, the model spends its effort sorting through unrelated code instead of explaining the actual implementation.

In a real-world scenario, low retrieval precision causes a few recurring problems:

1. Mixing unrelated implementations: When retrieval includes both legacy and current code paths, responses blend multiple approaches without making it clear which one is active. This leaves developers unsure which pattern to follow.

2. Pulling in test and support code: Test helpers, mocks, and error-handling utilities are often retrieved alongside production logic. When that happens, responses describe test behavior as if it were part of the runtime system.

3. Relying on documentation instead of code: Documentation and README files sometimes replace implementation files in the retrieved context. The result is a high-level explanation that doesn’t reflect how the feature actually works in code.

These issues don’t always show up in recall-focused metrics. A system can retrieve many relevant files and still produce weak answers if most of the context is not directly tied to the implementation. The rest of this benchmark measures how often that happens and how it affects task completion across real development queries.

Ground Truth

To evaluate retrieval accuracy, we defined a fixed set of ground truth files for each query representing the minimum implementation context required to answer correctly.

Ground truth came from manual Next.js codebase analysis. Files were included if they contained primary implementation logic, coordinated multi-component execution, or defined runtime entry points. Test files, documentation, type definitions without behavior, and deprecated code were excluded.

Ground Truth Mapping

Retrieval results were evaluated using standard information retrieval metrics:

1. Precision (percentage of retrieved files in ground truth)

2. Recall (percentage of ground truth files retrieved)

3. F1 score (balanced measure combining both). Perfect scores are required for retrieving all ground truth files without irrelevant additions.

Context Precision Test

Retrieval accuracy depends on whether the agent identifies core implementation files or includes irrelevant context. We executed each query with both systems (Cursor & ByteRover) and compared retrieved files against ground truth to measure precision, recall, and F1 scores.

Aggregate Results

ByteRover achieved complete retrieval (100% F1) on seven queries compared to Cursor's single perfect score, but the performance gap came from consistent noise in manual tagging, where developers included adjacent files, documentation, or test utilities that appeared relevant through keyword matching but lacked core implementation logic.

Precision vs Recall Distribution

The aggregate results highlight ByteRover's advantage but looking at how precision and recall balance across queries shows where each method performs well or struggles.

Cursor's queries scattered across the precision-recall space. Q1 and Q8 fell below 35% precision due to heavy noise, while Q4 and Q6 achieved high precision (75-100%) only on concentrated implementations. ByteRover maintained consistently high precision (≥80% on 9/10 queries) and high recall (≥80% on 8/10 queries). Memory-based retrieval maintained both, sacrificing precision to maximize recall. The tight clustering near (100%, 100%) shows predictable performance, while Cursor's variance indicates unpredictable results depending on whether developers correctly guessed file locations.

Per-Category Breakdown

We also broke down the results by category to identify where manual tagging introduced noise and where automated retrieval maintained precision across varying implementation patterns.

Cross-Module Features:

Manual tagging was least reliable for features spread across multiple files. In those cases, keyword search pulled in the nearby infrastructure files rather than the core implementation.

In these cases, Cursor often retrieved generic server setup files (such as base-server.ts or next-server.ts) or cache interfaces, while missing the files where rendering, revalidation, or cache updates actually occur.

Single-Module Features:

When a feature was in a few clearly named files, manual tagging worked well and was similar to automated retrieval.

These queries had obvious entry points, so both approaches tended to retrieve the same implementation files with little noise.

Build Infrastructure:

Build tooling showed mixed results, where even curated memory couldn't always narrow the scope. It happened because the logic is spread across configuration files, loaders, and helper utilities.

In these cases, it was difficult to separate the main configuration from supporting code, since build files often reference many utilities without clearly indicating which parts are essential. Like, webpack configuration queries have unclear boundaries between core config and supporting utilities.

Framework Pattern Comparison:

These queries required knowing which implementation applies today, not just which files mention the feature.

Manual tagging often retrieved files from both older and newer implementations, along with shared server code, which led to mixed explanations instead of a clear description of the current pattern.

In all categories, the gap got bigger when queries needed an understanding of relationships between files instead of just matching keywords. Cross-module features and framework comparisons had the biggest precision gaps (63pp and 45pp, respectively) because manual tagging couldn't distinguish between core logic and supporting infrastructure. Single-module features did best for both methods since clear file names made selection easy. The build infrastructure category was tough for both systems, showing the natural confusion in configuration files that reference many utilities without clear limits.

Retrieval Examples

Beyond the per-query scores, we examined how each system behaved during execution. The example below uses Q8 (ISR revalidation) to illustrate the difference between keyword-based file selection and memory-based retrieval.

Q8: ISR Revalidation Logic

Query: "Where is ISR (Incremental Static Regeneration) revalidation logic implemented?"

Cursor Approach: File Search with Manual Tagging

Cursor executed a multi-step search process:

1. Read 4 initial tagged files (pages-handler.ts, revalidate.ts, base-server.ts, index.ts)

2. Performed 6 codebase searches using keywords like "isStale | revalidate | incremental" and "handleResponse | ResponseCache | incrementalCache"

3. Read 4 additional files (index.ts, revalidation-utils.ts, route-module.ts, index.ts)

4. Checked base-server.ts and build/index.ts for additional context

Result: Retrieved 2 correct files (pages-handler.ts, revalidate.ts) but included base-server.ts and build/index.ts (orchestration layers, not core ISR implementation). Missing file-system-cache.ts despite extensive searches. Precision: 50% on tagged files.

ByteRover Approach: Memory Retrieval

Retrieved from curated memory (no codebase search required):

• index.ts (IncrementalCache core logic)

• action-handler.ts (executeRevalidates trigger)

• server-action-reducer.ts (client-side cache eviction)

Result: All 3 files directly implement ISR revalidation across the complete flow: cache management → server-side triggers → client-side synchronization. Precision: 100%

Retrieval Source Comparison

ByteRover's memory layer was encoded during brv curate that ISR revalidation spans three implementation boundaries (cache storage, server action coordination, client state updates), eliminating the need for keyword-based searches that surface orchestration files matching "revalidate" terminology but lacking implementation logic.

Our Interpretation

Manual tagging failed on queries requiring cross-module understanding because developers relied on keyword search, which retrieved adjacent files: wrappers, utilities, tests, alongside implementation. ByteRover's one-time curation encodes structural patterns (which files implement features, how modules interact), eliminating the need to rediscover architecture per query.

Manual selection worked well when the required files were well-known entry points (metadata generation, font loader). The gap emerged when implementation boundaries weren't obvious from file names alone.

Task Completion Test

Retrieval precision determines whether AI models receive implementation files or noise, but the metric that matters for development teams is task completion, whether the final output actually solves the problem. We evaluated both systems by verifying their responses against query requirements using binary scoring, where answers either fully addressed the architectural question or failed to provide actionable implementation guidance.

Task Completion Results

ByteRover achieved complete task success across all queries, while Cursor failed on Q1 due to retrieval precision dropping to 25% F1. The response mixed correct files (app-render.tsx) with auxiliary modules (base-server.ts, next-server.ts, server-utils.ts) that matched the "server" keyword but lacked React Server Component rendering implementation, producing verbose explanations that covered HTTP server initialization instead of RSC architecture.

Correlation Between Retrieval Precision and Answer Quality

Cursor's single failure occurred at 25% F1 (Q1). In this case, three noise files (base-server.ts, next-server.ts, server-utils.ts) overwhelmed one correct file (app-render.tsx), causing the AI to explain server initialization instead of React rendering.

When Cursor's F1 stayed above 60%, it answered correctly despite including extra files. The AI successfully filtered noise in 9 out of 10 queries, but failed when the wrong files outnumbered the right ones.

ByteRover maintained correct answers even when retrieval wasn't perfect. At 66.7% F1 (Q4, Q9), ByteRover missed some relevant files but didn't include wrong ones. This matters because missing context produces incomplete answers, while wrong context produces incorrect answers. ByteRover's 91.7% average precision meant responses focused on actual implementation, while Cursor's 60.0% precision required filtering irrelevant files, a task the AI handled well except when noise dominated.

Scatter plot showing F1 Score (x-axis) vs Answer Correctness (y-axis, binary 0/1) for both systems. Cursor shows a single failure point at 25% F1, ByteRover shows a perfect horizontal line at correctness=1.0 across all F1 ranges.

False Positive Impact on Response Quality

Cursor's false positives clustered into three categories that degraded answer quality in measurable ways:

ByteRover eliminated these categories through architectural encoding during curation, where the memory layer compressed knowledge about which files contain active implementation versus supporting infrastructure.

This architectural encoding also enables reusing context across related queries instead of rescanning the codebase each time.

Redundant Context Fetching

In addition to retrieval precision and reasoning accuracy, we evaluated how each system handles repeated architectural context across related queries.

Developers often ask related questions in sequence. Someone exploring cache invalidation might ask, "How is router cache structured?" followed by "Where does ISR revalidation trigger?" Traditional retrieval approaches treat each query independently, re-scanning the codebase and retrieving overlapping files multiple times. ByteRover's memory layer recognizes architectural relationships and reuses context across related queries.

We measured redundancy by analyzing query pairs that share implementation context, cases where the second query requires files already retrieved for the first.

Query Pair Analysis

Across these three query pairs, Cursor retrieved six files twice (12 total retrievals) because manual tagging requires developers to identify relevant files for each question independently. ByteRover retrieved those same six files once (6 total retrievals), reducing redundant context fetching by 50% through memory-based reuse.

Total Redundant Retrievals:

• Cursor: 6 files × 2 retrievals = 12 total

• ByteRover: 6 files × 1 retrieval = 6 total

• Redundancy eliminated: 50%

This 50% reduction compounds during real development sessions where engineers ask 15-20 related questions while debugging a feature or exploring architectural patterns. For the token usage implications of this redundancy, including concrete cost calculations across team sizes, see our previous benchmark on token efficiency.

Reducing redundancy limits how often the same context is loaded, but it does not guarantee that the retrieved context is accurate. That’s where Precision Matters.

How Precision Affects Answer Quality

After looking at the results for each query and how often tasks were completed, we can see patterns showing how precision levels affect whether answers succeed or fail. The three failure patterns described earlier (mixing unrelated implementations, pulling in test code, and relying on documentation) happened differently based on how much precision decreased.

Q7 (API routes vs Route Handlers) demonstrated this directly. Cursor retrieved both api-resolver.ts (Pages Router) and app-route/module.ts (App Router), but also included base-server.ts, which predates both routing systems. The response mixed three architectural patterns without clarifying which applies to modern development.

Q8 (ISR revalidation) showed another failure mode where Cursor retrieved handle-isr-error.tsx alongside file-system-cache.ts, causing the model to reference test fixtures as production implementation.

ByteRover's memory layer filtered these categories during brv curate by encoding which files contain active implementation versus supporting infrastructure, deprecated patterns, or test utilities. The correlation between precision and task completion quantifies this relationship:

Cursor's single failure occurred at 25% F1 (Q1), where three noise files (base-server.ts, next-server.ts, server-utils.ts) overwhelmed one correct file (app-render.tsx), producing a response about HTTP server initialization instead of React Server Component rendering. ByteRover maintained precision above 80% on nine queries by encoding architectural relationships rather than matching keywords.

Limitations

This benchmark tested how accurately information was retrieved for ten architectural questions on Next.js. Several limitations affect how broadly these results can be applied:

Query scope: Ten tasks captured common development patterns but missed edge cases like historical reasoning, cross-repository analysis, or documentation-heavy queries.

Ground truth subjectivity: We defined ground truth through manual codebase analysis. Another engineer might include more context files; the 91.3% F1 score reflects alignment with our criteria, not absolute correctness.

Codebase specificity: Next.js is a modular framework with clear boundaries. ByteRover performed better on questions needing understanding across modules. Monolithic codebases with tightly connected logic might show different precision differences.

Static snapshot: Testing used a fixed repository state, ignoring how shared memory evolves, how teams collaborate on curation, or how retrieval adapts as architectures change.

These constraints narrow the scope but don't change the core finding: memory-based retrieval achieved 31-percentage-point higher F1 accuracy than manual file tagging on queries requiring cross-module architectural understanding.

Closing

This benchmark measured a straightforward question: Does automated retrieval based on curated architectural memory outperform manual file selection?

Across ten queries in four categories on the Next.js codebase, ByteRover reached 91.3% F1 accuracy, while Cursor got 60.0%. Manual tagging is effective for single-module features but has trouble with cross-module implementations and comparing framework patterns. Memory-based retrieval captures architectural relationships during curation, reducing noise from test utilities, documentation, and legacy code to provide clear implementation logic without needing developers to remember file locations.

Combined with the 83% token reduction shown in our previous benchmark, memory-based context delivers both efficiency and accuracy without trade-offs.

If your team experiences retrieval noise or context precision issues in AI coding workflows, ByteRover is worth testing:

Try ByteRover

Full setup guide here