01Section
What NLP Focuses On
NLP covers text, semantics, and language structure across tasks like classification, extraction, retrieval, generation, QA, and dialogue.
Text generation ability is not the same as stable business-task completion.
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NLP
Understand NLP through text representation, task types, output standards, and application boundaries so you can choose between rules and models pragmatically.
02Section
Choosing Rules or Models
Small stable tasks with clear boundaries often suit rules better, while semantic variation favors models.
Mature systems often combine both approaches instead of choosing only one.
03Section
Define the Output Standard First
Define the output standard before deciding whether the task is extraction, classification, or generation.
Once the target output is clear, data prep, evaluation, and deployment strategy become much easier.
04Section
Common Practical Scenarios
Common scenarios include intent detection, knowledge QA, summarization, keyword extraction, moderation, and structured information extraction.
They all use text, but the engineering focus differs, so business goals should be made explicit early.
05Section
Common Pitfalls
Common pitfalls include vague task definitions, inconsistent evaluation, and confusing generation ability with business-task reliability.
Without output constraints, NLP systems often look impressive but fail to serve real workflows consistently.
06Section
How to Build the First Example
A good first NLP example is a tightly scoped task such as sentiment classification, keyword extraction, or fixed-format summarization.
Open-ended dialogue is a poor starting point because it stacks too many hard problems at once.
07Section
Recommended Learning Order
Start with classification and extraction, then move to retrieval and generation, and only later attempt dialogue or complex QA.
Early tasks are easier to evaluate because their output standards are clearer.
08Section
How It Combines with Other Topics
NLP often combines with LangChain, RAG, vector retrieval, and agents.
Once text tasks enter real systems, they usually need retrieval, output constraints, and post-processing rather than pure generation.