Extracting Meaning from Text
Implementing traditional NLP techniques like text classification, named entity recognition (NER), and sentiment analysis alongside modern LLMs.
Why NLP & Natural Language Processing Matters
While LLMs are powerful, traditional NLP techniques are often faster, cheaper, and more reliable for specific, bounded tasks.
| Market Signal | Impact Detail |
|---|---|
| Employer Demand | Required for specialized AI and Data Science roles. |
How We Use It
We use hybrid approaches, using efficient NLP models for preprocessing and routing, and invoking expensive LLMs only for complex reasoning tasks.
Real World Example
We implemented a lightweight NLP model to classify incoming support tickets with 95% accuracy, saving thousands in LLM API costs.
The Slickrock Advantage
"We know when NOT to use an LLM, using traditional NLP to optimize cost and latency."
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Frequently Asked Questions
What is Named Entity Recognition (NER)?
NER is an NLP task that identifies and classifies named entities (like people, organizations, dates) within unstructured text.