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.
Employer Demand
Required for specialized AI and Data Science roles.
How We Use It
We use hybrid approaches, utilizing 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, utilizing traditional NLP to optimize cost and latency."
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.