RAG & Retrieval
Positional Encoding
Positional encoding is the signal a transformer uses to know where each token sits in a sequence, since self-attention on its own treats input as an unordered set.
In depth
Positional encoding is the signal a transformer uses to know where each token sits in a sequence, since self-attention on its own treats input as an unordered set. The original Transformer added fixed sine and cosine waves of different frequencies to each token embedding. Most modern LLMs instead use Rotary Position Embedding (RoPE), which rotates query and key vectors by position, or ALiBi, which biases attention scores by distance, and the choice largely decides how well a model handles contexts longer than it was trained on.
Primary source: arxiv.org
Example
In practice, developers reach for Positional Encoding 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 Positional Encoding?
Positional encoding is the signal a transformer uses to know where each token sits in a sequence, since self-attention on its own treats input as an unordered set.
Why does Positional Encoding matter for AI developers?
Positional Encoding 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 Positional Encoding?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Positional Encoding. 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
RoPE (Rotary Position Embedding)
A way of encoding token positions in transformers by rotating each query and key vector by an angle proportional to its position.
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.
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