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+## Extraction Strategies 🧠
+
+Crawl4AI offers powerful extraction strategies to derive meaningful information from web content. Let's dive into two of the most important strategies: `CosineStrategy` and `LLMExtractionStrategy`.
+
+### CosineStrategy
+
+`CosineStrategy` uses hierarchical clustering based on cosine similarity to group text chunks into meaningful clusters. This method converts each chunk into its embedding and then clusters them to form semantical chunks.
+
+#### When to Use
+- Ideal for fast, accurate semantic segmentation of text.
+- Perfect for scenarios where LLMs might be overkill or too slow.
+- Suitable for narrowing down content based on specific queries or keywords.
+
+#### Parameters
+- `semantic_filter` (str, optional): Keywords for filtering relevant documents before clustering. Documents are filtered based on their cosine similarity to the keyword filter embedding. Default is `None`.
+- `word_count_threshold` (int, optional): Minimum number of words per cluster. Default is `20`.
+- `max_dist` (float, optional): Maximum cophenetic distance on the dendrogram to form clusters. Default is `0.2`.
+- `linkage_method` (str, optional): Linkage method for hierarchical clustering. Default is `'ward'`.
+- `top_k` (int, optional): Number of top categories to extract. Default is `3`.
+- `model_name` (str, optional): Model name for embedding generation. Default is `'BAAI/bge-small-en-v1.5'`.
+
+#### Example
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+# Define extraction strategy
+strategy = CosineStrategy(
+    semantic_filter="finance economy stock market",
+    word_count_threshold=10,
+    max_dist=0.2,
+    linkage_method='ward',
+    top_k=3,
+    model_name='BAAI/bge-small-en-v1.5'
+)
+
+# Sample URL
+url = "https://www.nbcnews.com/business"
+
+# Run the crawler with the extraction strategy
+result = crawler.run(url=url, extraction_strategy=strategy)
+print(result.extracted_content)
+```
+
+### LLMExtractionStrategy
+
+`LLMExtractionStrategy` leverages a Language Model (LLM) to extract meaningful content from HTML. This strategy uses an external provider for LLM completions to perform extraction based on instructions.
+
+#### When to Use
+- Suitable for complex extraction tasks requiring nuanced understanding.
+- Ideal for scenarios where detailed instructions can guide the extraction process.
+- Perfect for extracting specific types of information or content with precise guidelines.
+
+#### Parameters
+- `provider` (str, optional): Provider for language model completions (e.g., openai/gpt-4). Default is `DEFAULT_PROVIDER`.
+- `api_token` (str, optional): API token for the provider. If not provided, it will try to load from the environment variable `OPENAI_API_KEY`.
+- `instruction` (str, optional): Instructions to guide the LLM on how to perform the extraction. Default is `None`.
+
+#### Example Without Instructions
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+# Define extraction strategy without instructions
+strategy = LLMExtractionStrategy(
+    provider='openai',
+    api_token='your_api_token'
+)
+
+# Sample URL
+url = "https://www.nbcnews.com/business"
+
+# Run the crawler with the extraction strategy
+result = crawler.run(url=url, extraction_strategy=strategy)
+print(result.extracted_content)
+```
+
+#### Example With Instructions
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+# Define extraction strategy with instructions
+strategy = LLMExtractionStrategy(
+    provider='openai',
+    api_token='your_api_token',
+    instruction="Extract only financial news and summarize key points."
+)
+
+# Sample URL
+url = "https://www.nbcnews.com/business"
+
+# Run the crawler with the extraction strategy
+result = crawler.run(url=url, extraction_strategy=strategy)
+print(result.extracted_content)
+```
+
+#### Use Cases for LLMExtractionStrategy
+- Extracting specific data types from structured or semi-structured content.
+- Generating summaries, extracting key information, or transforming content into different formats.
+- Performing detailed extractions based on custom instructions.
+
+For more detailed examples, please refer to the [Examples section](../examples/index.md) of the documentation.
+
+---
+
+By choosing the right extraction strategy, you can effectively extract the most relevant and useful information from web content. Whether you need fast, accurate semantic segmentation with `CosineStrategy` or nuanced, instruction-based extraction with `LLMExtractionStrategy`, Crawl4AI has you covered. Happy extracting! 🕵️‍♂️✨