RAG & Retrieval
Softmax
A function that converts a vector of raw scores into probabilities between 0 and 1 that sum to 1, by exponentiating each score and dividing by the sum of all the exponentials.
In depth
A function that converts a vector of raw scores into probabilities between 0 and 1 that sum to 1, by exponentiating each score and dividing by the sum of all the exponentials. In a language model it turns the logits at each step into a probability distribution over the vocabulary. It is also used inside attention to turn relevance scores into weights. Temperature works by dividing the logits before softmax, which sharpens or flattens the resulting distribution.
Primary source: docs.pytorch.org
Example
In practice, developers reach for Softmax when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
Common questions
What is Softmax?
A function that converts a vector of raw scores into probabilities between 0 and 1 that sum to 1, by exponentiating each score and dividing by the sum of all the exponentials.
Why does Softmax matter for AI developers?
Softmax sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Softmax?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Softmax. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
RAG & Retrieval
ALiBi
ALiBi (Attention with Linear Biases) is a way to give a transformer position information by penalizing attention scores in proportion to the distance between query and key, instead of adding positional embeddings to the token embeddings.
RAG & Retrieval
Semantic Search
A search method that finds results based on meaning rather than exact keyword matches.
RAG & Retrieval
Feed-Forward Network (FFN)
The feed-forward network is the per-token block in every transformer layer that follows attention: two linear projections with a nonlinearity between them, applied to each position independently.
Put this concept to work
Get Smarter About AI Dev
New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.
One email per weekReal code, not theoryFree forever