You will get LLM/RAG Observability Blind Spot Audit
Project details
LLM and RAG systems can look healthy while critical issues stay invisible: latency spikes, weak retrieval quality, token cost growth, missing prompt/response logs, silent errors, poor tracing, and no evaluation signals.
If you are not ready for a full LLMOps review yet, start with one architecture view, workflow, or screenshot. I can identify the most important observability blind spots and tell you what deserves deeper analysis before production risk increases.
I review your AI service flow, telemetry setup, monitoring signals, logging approach, RAG workflow, and cost/error visibility. You receive prioritized findings, risk framing, and actionable recommendations specific to LLM/RAG operations.
This project is ideal for AI startups, product teams, agencies, and backend teams running chatbots, RAG features, AI assistants, or LLM APIs. Higher tiers go deeper into telemetry gaps, retrieval quality, token cost visibility, and an LLMOps blueprint.
If you are not ready for a full LLMOps review yet, start with one architecture view, workflow, or screenshot. I can identify the most important observability blind spots and tell you what deserves deeper analysis before production risk increases.
I review your AI service flow, telemetry setup, monitoring signals, logging approach, RAG workflow, and cost/error visibility. You receive prioritized findings, risk framing, and actionable recommendations specific to LLM/RAG operations.
This project is ideal for AI startups, product teams, agencies, and backend teams running chatbots, RAG features, AI assistants, or LLM APIs. Higher tiers go deeper into telemetry gaps, retrieval quality, token cost visibility, and an LLMOps blueprint.
AI Development Type
Deep Learning, Knowledge Representation, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, MLflow, NVIDIA AI Platform, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$75
|
Standard
$250
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 5 days |
Number of Revisions | 0 | 1 | 0 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$25 - $50
Additional Revision
+$25
Additional Revision
(+ 1 Day)
+$25
Additional Service Flow (+1 Day)
(+ 1 Day)
+$40
Prompt/Response Logging Strategy
(+ 2 Days)
+$50Frequently asked questions
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Freddy is a HUGE find. It's very rare to find someone as seasoned and talented as Freddy is.
He is professional, cares for his clients, and will gain your REPEAT BUSINESS.
We ending contract as we promised to give him a good review but will be hiring again.
He is professional, cares for his clients, and will gain your REPEAT BUSINESS.
We ending contract as we promised to give him a good review but will be hiring again.
About Freddy Daniel
Senior Platform Engineer | Cloud Cost, Kubernetes, LLMOps
100%
Job Success
Santa Cruz de la Sierra, Bolivia - 3:24 pm local time
My work is strongest when a team needs senior judgment before making infrastructure changes, scaling an AI feature, touching production, or spending more on cloud resources.
Typical audits I handle:
- Cloud Cost Leak Check: oversized compute, idle resources, orphaned storage, weak tagging, missing budgets, Kubernetes waste.
- Kubernetes Readiness Review: probes, resource requests/limits, rollout safety, ingress, secrets, scaling, rollback readiness.
- LLMOps Observability Review: latency, token cost visibility, prompt/response logging, RAG blind spots, dashboards, alerting quality.
- MySQL / API / CI-CD Triage: disk pressure, slow queries, FastAPI reliability, Dockerfile risk, pipeline fragility.
I do not need sensitive credentials for an initial audit. A safe first pass usually works from screenshots, exports, logs, YAML files, code snippets, architecture diagrams, or non-sensitive configuration excerpts.
You get a clear report with prioritized findings, risk level, evidence reviewed, practical recommendations, and the safest next step:
- no immediate action,
- a focused implementation sprint,
- or monthly reliability/cost/observability support if the issue is recurring.
Relevant background:
- 20+ years across infrastructure, cloud, telecom platforms, DevOps, automation, databases and production operations.
- Senior IT Cloud Engineer experience with OpenStack, Docker and Kubernetes environments.
- 5.0 Upwork feedback on technical work, including MySQL/database and Python-related support.
If you need a careful senior review before touching production, send me the non-sensitive evidence and I will help you define the safest next step.
Steps for completing your project
After purchasing the project, send requirements so Freddy Daniel can start the project.
Delivery time starts when Freddy Daniel receives requirements from you.
Freddy Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Architecture Review
I review your architecture diagram, service flow, or description to understand your LLM/AI service stack.
Step 2: Blind Spot Identification
I identify observability gaps: missing latency signals, untracked retrieval quality, invisible costs, silent errors, and dashboard blindness.