Who should use the Understand natural language workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
A focused workflow to process and interpret natural language inputs, delivering intent and entity extraction.
Deliverable outcome
A clean, structured JSON output containing intent and entities ready for application use
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A clean, structured JSON output containing intent and entities ready for application use
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use ChatGPT to a clean, tokenized version of the input ready for analysis. Then, you pass the output to spaCy to a syntactic tree showing grammatical relationships between words. Then, you pass the output to Prodigy to a list of extracted entities with types and resolved references. Then, you pass the output to Llama (Large Language Model Meta AI) to a primary intent label (and optionally secondary intents) for the input. Then, you pass the output to MindMeld to a disambiguated interpretation with resolved references. Finally, Google AppSheet AI is used to a clean, structured json output containing intent and entities ready for application use.
Preprocess raw text input
A clean, tokenized version of the input ready for analysis
Perform syntactic parsing
A syntactic tree showing grammatical relationships between words
Extract named entities
A list of extracted entities with types and resolved references
Determine user intent
A primary intent label (and optionally secondary intents) for the input
Resolve ambiguities and context
A disambiguated interpretation with resolved references
Structure output as intent and entities
A clean, structured JSON output containing intent and entities ready for application use
Clean and normalize the input text to remove noise and standardize format. This includes lowercasing, removing punctuation, correcting typos, and splitting into sentences or tokens. Use libraries like NLTK or spaCy for tokenization and basic cleaning.
Why ChatGPT: ChatGPT can preprocess raw text input through its conversational interface, handling tasks like cleaning, normalization, and basic tokenization via natural language instructions.
Analyze the grammatical structure of the input to identify parts of speech and dependency relationships. Use a dependency parser to build a tree of how words relate (subject, object, modifiers). This step is critical for understanding sentence structure and resolving ambiguities.
Why spaCy: spaCy is explicitly listed with dependency parsing capability, making it the best fit for syntactic parsing.
Identify and classify named entities such as persons, organizations, locations, dates, and numerical values. Use a pre-trained NER model (e.g., spaCy, Stanford NER) or a fine-tuned transformer. For domain-specific needs, consider custom entity recognition with training data.
Why Prodigy: Prodigy is explicitly designed for Named Entity Recognition, making it the most direct choice.
Classify the overall purpose or goal of the input (e.g., question, command, request, complaint). Use a combination of rule-based patterns (e.g., starting with 'what is') and a trained intent classifier (e.g., logistic regression or BERT). For multi-intent inputs, segment the text and classify each part.
Why Llama (Large Language Model Meta AI): Llama is designed for natural language understanding and reasoning, which directly supports intent determination.
Handle ambiguous words or phrases by leveraging context from previous turns or external knowledge. Use word sense disambiguation (e.g., Lesk algorithm) or a language model to pick the most likely meaning. If a conversation history exists, incorporate it to resolve pronouns or ellipsis.
Why MindMeld: MindMeld is explicitly designed for dialogue state tracking and entity resolution, directly addressing ambiguity and context resolution.
Package the extracted intent and entities into a structured JSON or dictionary format. Include the intent label, confidence score, and a list of entities with their types and spans. Validate that all required fields are present and format consistently for downstream use.
Why Google AppSheet AI: Google AppSheet AI can structure natural language into SQL schemas and structured outputs, aligning with intent and entity structuring.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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