Here’s a practical, step-by-step approach you can use to detect and analyze which prompts mention your brand — usable for small datasets or at scale. I’ll cover data collection, detection methods (exact + fuzzy + semantic), tooling/implementation suggestions, evaluation, and monitoring.

1. Collect & normalize your prompt data

• Gather prompts from every source: product logs, analytics, customer support transcripts, copywriters, social media, fine-tuning datasets, etc.
• Normalize text: lowercase, trim whitespace, remove control characters, normalize punctuation and diacritics (e.g., “Café” → “cafe”).
• Store metadata with each prompt: timestamp, user id (if allowed), channel, model version, session id, QA label if available.

2. Decide what “include my brand” means

• Exact brand token (e.g., “AcmeCo”).
• Variants: stylized names, spacing, punctuation, domain names, hashtags, mentions (@Acme), abbreviations (AC).
• Misspellings or OCR errors.
• Semantic references that imply the brand without name (e.g., “your smartphones that released in 2025” if only you have that product). Define categories you want to detect (exact, variant, misspelling, semantic reference).

3. Detection techniques (use a mix)

• Exact matching (fast, high precision)
  • Simple substring or tokenized match after normalization.
  • Use word-boundary checks to avoid false positives (e.g., “acme” in “macmeow”).
• Regex patterns for variants
  • Catch punctuation, spaces, hyphens, domains, hashtags, @mentions and common abbreviations. Example: \b(a[cC]me[-_\s]?co|AcmeCo.com|#AcmeCo|@AcmeCo)\b
• Fuzzy matching for misspellings
  • Levenshtein distance, fuzzy ratio thresholds (e.g., fuzzywuzzy, rapidfuzz). Useful for short brand names. Tune threshold (e.g., 80+) to balance false positives.
• Named Entity Recognition (NER)
  • Use spaCy or similar to detect ORG entities, then check against brand list.
• Semantic/embedding search for implicit mentions
  • Convert prompts and brand-description(s) to embeddings (Sentence-BERT, OpenAI embeddings, etc.). Compute cosine similarity and flag prompts above a threshold as “semantically referencing” the brand.
  • Helpful to catch paraphrases and contextual references.
• Rule-based/heuristic detection
  • If a prompt contains product names or unique model numbers that only you use, mark as brand mention.

4. Practical implementation plan

• Small scale / quick:
  • Pipeline: normalize → exact + regex → fuzzy for misses → manual review for edge cases.
  • Tools: Python with regex, rapidfuzz, pandas.
• Medium/large scale:
  • Add NER + embeddings for better recall.
  • Use a vector DB (e.g., Pinecone, Milvus, Weaviate, or PostgreSQL + PGVector) for fast semantic lookup.
  • Batch process embeddings and incremental updates for new prompts.
• Real-time / production:
  • Run lightweight checks (exact + regex) inline; send to async job for heavier checks (embeddings/fuzzy).
  • Use message queues and microservices so expensive checks don’t block user-facing flows.

5. Example code sketches

• Exact + regex (Python):
  • Normalize: s = re.sub(r'\s+', ' ', s.lower().strip())
  • Regex: pattern = re.compile(r'\b(acme|acme[-_\s]?co|AcmeCo.com|#acme)\b')
  • if pattern.search(s): mark_brand=True
• Fuzzy with rapidfuzz:
  • from rapidfuzz import fuzz
  • if fuzz.partial_ratio(s, "AcmeCo") > 85: mark_brand=True
• Embedding similarity (conceptual):
  • brand_emb = embed("AcmeCo, manufacturer of X product lines")
  • prompt_emb = embed(prompt_text)
  • sim = cosine(prompt_emb, brand_emb)
  • if sim > 0.75: semantic_brand_reference=True (Tune thresholds with validation set.)

6. Evaluation — measure and tune

• Create a labeled validation set (manual labeling) with examples of:
  • True brand mentions (exact and implicit)
  • Non-mentions (false positives like similar words)
• Key metrics: precision, recall, F1. Choose threshold to match business needs:
  • If you must not miss brand mentions (legal, safety), favor higher recall (but plan for more manual review).
  • If you must avoid false alerts, favor higher precision.
• Error analysis: review false positives and false negatives to add regex rules, synonyms, or fine-tune embedding thresholds.

7. Analytics & reporting Track over time:

• Volume of prompts with brand mentions (daily/weekly).
• Distribution by source/channel, geography, user segment.
• Sentiment of brand-mention prompts (use a sentiment model).
• Topics co-occurring with brand mentions (topic modeling / clustering).
• Trends and spikes (alerts when mentions suddenly increase).

8. Scaling & performance tips

• Precompute normalized tokens and embeddings on ingestion.
• Use inverted indices for token/regex search and vector DB for semantic queries.
• Batch embed new prompts to reduce API calls.
• Cache results for duplicate prompts.

9. Privacy, compliance & sampling

• Respect user privacy and regional laws (GDPR/CCPA): anonymize or hash PII.
• If you can’t store raw prompts, store features (embeddings, flags) or hash tokens for detection.
• Use sampling and rate limits for human review to control costs.

10. Tooling suggestions

• Quick/DIY: Python (pandas, regex, rapidfuzz), spaCy for NER.
• Embeddings/semantic search: OpenAI embeddings or open-source SBERT + vector DB (Pinecone, Milvus, Weaviate, PGVector).
• Monitoring / dashboards: Looker, Grafana, Superset, or data warehouses (BigQuery/Redshift/Snowflake).
• MLOps: airflow/Prefect for pipelines, Kafka for streaming.

11. Example workflow summary (recommended)

• Ingest & normalize prompts.
• Run fast checks: exact match + regex + NER.
• If not matched, run fuzzy match.
• If still ambiguous or high-value, run semantic embedding similarity.
• Flag results with confidence score and category (exact, fuzzy, semantic).
• Store results + metadata, run analytics & trigger alerts for abnormal spikes.
• Periodically retrain/tune thresholds using labeled data.

If you want, I can:

• Provide a ready-to-run Python notebook that implements exact, regex, fuzzy, and embedding detection on sample prompts.
• Help design the regex list and fuzzy thresholds for your specific brand name(s).
• Suggest a monitoring/dashboard layout for tracking brand mentions.

Which would you like next?