RRepoGEO

REPOGEO REPORT · LITE

PacktPublishing/LLM-Engineers-Handbook

Default branch main · commit 28a1ca0c · scanned 6/21/2026, 3:17:56 AM

GitHub: 5,119 stars · 1,228 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface PacktPublishing/LLM-Engineers-Handbook, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Clarify repository description as a book's code companion

    Why:

    CURRENT
    The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
    COPY-PASTE FIX
    Official code repository for 'The LLM Engineer's Handbook': practical examples for building and deploying advanced LLM and RAG apps to AWS using LLMOps best practices.
  • highreadme#2
    Add explicit purpose statement to README introduction

    Why:

    COPY-PASTE FIX
    This repository provides practical, production-ready code examples and best practices from 'The LLM Engineer's Handbook' to help you build, deploy, and monitor advanced LLM and RAG applications on AWS.
  • mediumtopics#3
    Add 'learning-resource' topic

    Why:

    CURRENT
    aws, fine-tuning-llm, genai, llm, llm-evaluation, llmops, ml-system-design, mlops, rag
    COPY-PASTE FIX
    aws, fine-tuning-llm, genai, llm, llm-evaluation, llmops, ml-system-design, mlops, rag, learning-resource

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface PacktPublishing/LLM-Engineers-Handbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
prometheus/prometheus
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. prometheus/prometheus · recommended 2×
  2. grafana/grafana · recommended 2×
  3. iterative/dvc · recommended 2×
  4. mlflow/mlflow · recommended 2×
  5. Amazon SageMaker · recommended 1×
  • CATEGORY QUERY
    How to deploy and monitor production-ready LLM and RAG applications on AWS using MLOps?
    you: not recommended
    AI recommended (in order):
    1. Amazon SageMaker
    2. SageMaker JumpStart
    3. SageMaker Pipelines
    4. AWS Lambda
    5. Amazon API Gateway
    6. Amazon DynamoDB
    7. Aurora
    8. Amazon OpenSearch Service
    9. Aurora PostgreSQL with pgvector
    10. CloudWatch Logs
    11. CloudWatch Metrics
    12. AWS Step Functions
    13. CodePipeline
    14. CodeBuild
    15. Amazon EKS (Elastic Kubernetes Service)
    16. Kubeflow (kubeflow/kubeflow)
    17. Docker
    18. Prometheus (prometheus/prometheus)
    19. Grafana (grafana/grafana)
    20. CloudWatch Container Insights
    21. Kubeflow Pipelines (kubeflow/pipelines)
    22. Argo Workflows (argoproj/argo-workflows)
    23. Amazon Bedrock
    24. AWS Fargate
    25. Amazon ECS
    26. Amazon S3
    27. DVC (Data Version Control) (iterative/dvc)
    28. SageMaker Feature Store
    29. MLflow (mlflow/mlflow)
    30. SageMaker Experiments
    31. SageMaker Model Registry
    32. Pinecone
    33. Weaviate (weaviate/weaviate)
    34. Hugging Face
    35. AWS Glue
    36. AWS IAM
    37. AWS KMS
    38. Amazon VPC

    AI recommended 38 alternatives but never named PacktPublishing/LLM-Engineers-Handbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for building an end-to-end LLM system, including fine-tuning and evaluation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Datasets (huggingface/datasets)
    2. Label Studio (heartexlabs/label-studio)
    3. Snorkel (snorkel-team/snorkel)
    4. DVC (Data Version Control) (iterative/dvc)
    5. MLflow (mlflow/mlflow)
    6. LoRA (Low-Rank Adaptation)
    7. QLoRA (Quantized LoRA)
    8. Prompt Tuning/Prefix Tuning
    9. Hugging Face Transformers (huggingface/transformers)
    10. OpenAI API
    11. Weights & Biases (W&B) (wandb/wandb)
    12. Optuna (optuna/optuna)
    13. PyTorch (pytorch/pytorch)
    14. CUDA
    15. DeepSpeed (microsoft/DeepSpeed)
    16. FSDP (Fully Sharded Data Parallel)
    17. ROUGE
    18. BLEU
    19. BERTScore (Tiiiger/bert_score)
    20. METEOR
    21. RAGAS (Ragas-AI/ragas)
    22. TruLens (trulens/trulens)
    23. Hugging Face Evaluate (huggingface/evaluate)
    24. LangChain Evaluation (langchain-ai/langchain)
    25. DeepEval (confident-ai/deepeval)
    26. Argilla (argilla-io/argilla)
    27. Garak (leondf/garak)
    28. Hugging Face TGI (Text Generation Inference) (huggingface/text-generation-inference)
    29. vLLM (vllm-project/vllm)
    30. NVIDIA Triton Inference Server (triton-inference-server/server)
    31. AWS SageMaker
    32. Google Cloud Vertex AI
    33. Azure Machine Learning
    34. LangChain Observability (LangSmith) (langchain-ai/langsmith)
    35. Prometheus (prometheus/prometheus)
    36. Grafana (grafana/grafana)
    37. Datadog
    38. New Relic
    39. Git

    AI recommended 39 alternatives but never named PacktPublishing/LLM-Engineers-Handbook. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of PacktPublishing/LLM-Engineers-Handbook?
    pass
    AI did not name PacktPublishing/LLM-Engineers-Handbook — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts PacktPublishing/LLM-Engineers-Handbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PacktPublishing/LLM-Engineers-Handbook explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo PacktPublishing/LLM-Engineers-Handbook solve, and who is the primary audience?
    pass
    AI did not name PacktPublishing/LLM-Engineers-Handbook — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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PacktPublishing/LLM-Engineers-Handbook — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite