1. Query Preprocessing and Understanding:

Query Understanding: The foundational step where the search engine attempts to understand the user's intent and the meaning of the query.
Language Identification: Detecting the language of the query to apply appropriate linguistic processing.
Character Filtering: Cleaning the query by removing or normalizing irrelevant or harmful characters.
Tokenization: Breaking down the query into individual words or tokens for further analysis.
Spelling Correction: Identifying and correcting spelling errors to ensure the query reflects the user's intended search.
Stemming and Lemmatization: Reducing words to their base or root form to improve the matching process.
Entity Recognition: Identifying named entities (people, places, organizations) within the query to enhance understanding and retrieval accuracy.
Taxonomies and Ontologies: Utilizing structured knowledge frameworks to interpret the query within a broader context of related concepts and relationships.

2. Query Modification and Expansion:

Query Rewriting: Modifying the original query to improve its effectiveness, potentially including synonym replacement or rephrasing.
Query Expansion: Adding related terms or phrases to the query to broaden its scope and retrieve more comprehensive results.
Query Relaxation: Simplifying or broadening the query criteria to ensure results are returned even when exact matches are not found.
Query Segmentation: Breaking the query into meaningful segments or phrases to understand the relationship between different parts of the query.
Query Scoping: Determining the specific subset of data or indexes that the query should target based on its content or user context.

3. Contextual and Personalized Processing:

Contextual Query Understanding: Incorporating additional context (such as the user's previous queries or the current webpage) to better interpret the query.
Session Context: Using data from the current search session to tailor search results and suggestions.
Location as Context: Considering the user's geographic location to deliver more relevant local results.
Seasonality: Adjusting search responses based on seasonal trends or events relevant to the query.
Personalization: Tailoring search results and suggestions based on the user's historical behavior and preferences.

4. Interactive Search Features:

Autocomplete and User Experience: Predicting and suggesting possible completions for the user's query to speed up the search process.
Search as a Conversation: Facilitating a more interactive search experience, where the system and user engage in a dialogue to refine the search.
Clarification Dialogues: Asking follow-up questions to clarify the user's intent when the initial query is ambiguous.
Relevance Feedback: Allowing users to provide feedback on the relevance of results to refine future searches.

5. Result Processing and Presentation:

Faceted Search: Enabling users to refine search results based on specific attributes or categories.
Search Results Presentation: The overall layout and presentation of search results to the user.
Search Result Snippets: Generating short previews or summaries of each result to help users assess relevance.
Search Results Clustering: Grouping similar results to help users navigate large sets of results more effectively.
Question Answering: Providing direct answers to queries phrased as questions, often extracted from documents or generated by AI.