feat: define latency SLOs for AI story generation pipelinedocs: define latency SLOs

[Jass-pvt]

Adds documented latency SLOs for the end-to-end AI story generation pipeline.

Defines p95 targets for:

• API request handling
• Groq generation
• Streaming first token
• MongoDB writes
• UI render completion

This establishes the performance baseline for future observability
and dashboard implementation.

Summary by CodeRabbit

• Documentation
  • Added comprehensive performance documentation establishing latency targets and benchmarks across the entire story rendering pipeline. Defines clear end-to-end performance expectations and provides detailed stage-level metrics spanning API request handling, Groq API integration, token streaming, database operations, and UI rendering completion. Establishes transparent performance standards.

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[coderabbitai]

📝 Walkthrough

Walkthrough

A new documentation file has been added that specifies performance latency targets (SLOs) for GroqTales. It defines end-to-end p95 latency targets of under 4000ms for story rendering and breaks down stage-level targets for API handling, Groq API calls, token streaming, database writes, and UI rendering.

Changes

Cohort / File(s)	Summary
Performance Documentation GroqTales/docs/PERFORMANCE.md	New file documenting end-to-end and stage-level latency targets (SLOs) with p95 percentile benchmarks, rationale for metric selection, and use cases for performance monitoring and regression detection.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Poem

🐰 Hop, hop, latency we measure true,
Four seconds end-to-end, that's our view,
Each stage defined with p95 grace,
Performance targets set the pace!
Bottlenecks exposed, dashboards bright,
Stories rendered oh so right!

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 inconclusive)

Check name	Status	Explanation	Resolution
Description check	❓ Inconclusive	The PR description is vague and lacks required template sections like Issue Reference, detailed Summary of Changes, Context/Motivation, Type of Change selection, Technical Checklist completion, and mandatory Final Acknowledgements.	Complete the full PR description template, including Issue Reference, detailed Summary of Changes explaining why these SLOs were chosen, Type of Change selection (Documentation), and all mandatory Final Acknowledgements checkboxes.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically describes the main change: introducing latency SLOs for the AI story generation pipeline, which matches the pull request's core objective of defining performance targets.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

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✨ Finishing Touches

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[coderabbitai]

Actionable comments posted: 1

🧹 Nitpick comments (2)

GroqTales/docs/PERFORMANCE.md (2)

1-103: Collapse the excessive blank lines in the Markdown source.

The file currently has a blank line after every single content line, making the raw source significantly harder to read and edit. Standard Markdown only requires a single blank line to separate paragraphs or sections; consecutive blank lines render identically to one. Consider consolidating to a single blank line between sections.

✏️ Example of cleaned-up structure

-\# Performance \& Latency Budget
-
-
-
-GroqTales defines Service Level Objectives (SLOs) for the AI story generation
-
-pipeline to ensure a fast and responsive user experience.
-
-
-
-This document outlines the expected latency targets across the full stack:
-
-UI → API → Groq → Database → UI Render.
+# Performance & Latency Budget
+
+GroqTales defines Service Level Objectives (SLOs) for the AI story generation
+pipeline to ensure a fast and responsive user experience.
+
+This document outlines the expected latency targets across the full stack:
+UI → API → Groq → Database → UI Render.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@GroqTales/docs/PERFORMANCE.md` around lines 1 - 103, The Markdown source has
an extra blank line after almost every line making it hard to read; edit
PERFORMANCE.md to collapse consecutive blank lines into single blank lines while
preserving required spacing for headings and the table structure (e.g., around
"# Performance & Latency Budget", "## 🎯 End-to-End Latency Target", the
stage-level table, and other section headings). Ensure no more than one blank
line separates paragraphs or sections, keep the table rows intact and
contiguous, and remove only superfluous empty lines so the rendered output is
unchanged but the raw file is compact and readable.

---

85-99: Add measurement methodology and alerting thresholds to make these SLOs actionable.

Defining targets without specifying how they are measured leaves the SLOs unenforceable. Consider adding a short section covering:

• Instrumentation: Where each stage is timed (e.g., server-side middleware for API handling, Date.now() deltas around Groq stream open/close, Mongoose plugin hooks for MongoDB writes, PerformancePaintTiming / PerformanceObserver for UI render).
• Aggregation window: The percentile window over which p95 is computed (e.g., rolling 7-day, per-deploy window).
• Alerting threshold: At what breach rate or sustained violation period an alert fires (e.g., "alert if p95 exceeds target for >5% of requests in a 1-hour window").
• Error budget: What percentage of requests can miss the SLO before action is required.
• Review cadence: How often these targets are revisited (e.g., after each major Groq model upgrade or infrastructure change).

Without these, the "Performance Dashboard" referenced in the Purpose section has no defined breach condition to surface.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@GroqTales/docs/PERFORMANCE.md` around lines 85 - 99, Add concrete measurement
methodology and alerting thresholds to the "📌 Purpose" / SLOs section so
targets are actionable: specify instrumentation points (e.g., server-side
middleware timing for API handling, Date.now() deltas around Groq stream
open/close, Mongoose plugin hooks for MongoDB writes, PerformancePaintTiming /
PerformanceObserver for UI render), define aggregation windows for percentiles
(e.g., rolling 7-day or per-deploy for p95), state alerting thresholds and
breach conditions (e.g., alert if p95 exceeds target for >5% of requests in a
1-hour window), declare an error budget percentage allowed before remedial
actions, and set a review cadence (e.g., after each major Groq model upgrade or
infrastructure change) so the Performance Dashboard can surface meaningful
breaches.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@GroqTales/docs/PERFORMANCE.md`:
- Around line 29-57: The stage-level p95 targets in the "🔎 Stage-Level Latency
Targets" table sum to 4100ms which violates the documented E2E Target (p95): <
4000ms; update the table and narrative around the "Groq API Call" and "First
Token Streaming" rows to either (A) mark "First Token Streaming" as a
sub-interval of "Groq API Call" (e.g., add a note/column or indentation
indicating TTFT is included in Groq API Call) so the sequential sum becomes
3100ms, or (B) increase the E2E Target to at least the summed stage budget
(≥4100ms) and add a sentence noting additional network hop allowances; also
explicitly mention network transit latency between client→server and server→Groq
in the PERFORMANCE.md text so the E2E target and stage budgets are reconciled.

---

Nitpick comments:
In `@GroqTales/docs/PERFORMANCE.md`:
- Around line 1-103: The Markdown source has an extra blank line after almost
every line making it hard to read; edit PERFORMANCE.md to collapse consecutive
blank lines into single blank lines while preserving required spacing for
headings and the table structure (e.g., around "# Performance & Latency Budget",
"## 🎯 End-to-End Latency Target", the stage-level table, and other section
headings). Ensure no more than one blank line separates paragraphs or sections,
keep the table rows intact and contiguous, and remove only superfluous empty
lines so the rendered output is unchanged but the raw file is compact and
readable.
- Around line 85-99: Add concrete measurement methodology and alerting
thresholds to the "📌 Purpose" / SLOs section so targets are actionable: specify
instrumentation points (e.g., server-side middleware timing for API handling,
Date.now() deltas around Groq stream open/close, Mongoose plugin hooks for
MongoDB writes, PerformancePaintTiming / PerformanceObserver for UI render),
define aggregation windows for percentiles (e.g., rolling 7-day or per-deploy
for p95), state alerting thresholds and breach conditions (e.g., alert if p95
exceeds target for >5% of requests in a 1-hour window), declare an error budget
percentage allowed before remedial actions, and set a review cadence (e.g.,
after each major Groq model upgrade or infrastructure change) so the Performance
Dashboard can surface meaningful breaches.

[Drago-03]

Not ready to merge.

• E2E p95 is < 4000ms, but stage p95s sum to 4100ms. Either make “First Token Streaming” explicitly part of the Groq API call or adjust the E2E target and justify the extra budget.
• Add a short “Measurement & Alerting” section: instrumentation per stage, p95 window, alert thresholds/error budget, review cadence.
• Clean up extra blank lines in PERFORMANCE.md so there’s at most one blank line between sections and the table is contiguous.
• Fill out the full PR description template and link the related issue.