If you’ve been using AI tools to help explain concepts like BGP, troubleshoot routing behavior, or draft migration notes, you’ve probably seen the term tokens. Tokens are the real unit AI models “read” and “write,” and understanding them is the difference between getting fast, clean answers and watching the model ramble its way into unnecessary cost and lost context.
This post breaks tokens down in plain terms, using networking examples, but keeps the tone approachable for anyone who’s still learning the technology.
What Is a Token (Really)?
A token is not the same thing as a word.
AI models don’t process text as words the way humans do. Instead, they break text into chunks. Sometimes a chunk is a full word, sometimes it’s part of a word, and sometimes it’s punctuation or spacing.
At a high level:
- Short, common words are often a single token
- Longer or less common words may be split into multiple tokens
- Punctuation and symbols usually count as tokens
- Common technical terms tend to tokenize efficiently
A practical rule of thumb:
- 1 token ≈ 4 characters of English text
- 1 token ≈ 0.75 words
This isn’t exact, but it’s accurate enough for planning and estimating.
How Many Tokens Is a Simple Network Question?
Consider this question:
“Tell me how BGP makes routing path decisions.”
This is typically around 10 tokens.
Most of the words are short and common, and BGP itself is usually treated as a single token.
A rough breakdown looks like this:
- Tell (1)
- me (1)
- how (1)
- BGP (1)
- makes (1)
- routing (1–2)
- path (1)
- decisions (2)
- . (1)
The exact count can vary slightly, but it lands close to ten.
The key point: the question is almost never where token usage becomes a problem. The answer is.
The Real Token Burn: The Answer
Short prompts often lead to long responses.
A single-sentence question can easily produce a response that’s hundreds of tokens long, especially for topics like BGP that involve decision processes, attributes, and design tradeoffs.
In practice:
- Your prompt might be 10–30 tokens
- The model’s response might be 500–1,200 tokens
If you care about cost, response size, or keeping a conversation focused, the most effective control is bounding the output.
How Prompt Wording Changes Token Usage
Even small wording changes can increase token count without improving the result.
Compare:
- “Explain BGP path selection.”
- “From a routing protocol standpoint, explain in detail how BGP evaluates and selects optimal routing paths.”
Both prompts ask for the same thing. The second just burns more tokens and often triggers a more verbose response.
AI models don’t need extra filler to understand intent. Clear, direct language almost always works better.
Why Networking Terms Are Token-Efficient
Networking terminology tends to be well represented in training data, which means common terms usually tokenize efficiently.
Examples that are often one or two tokens:
- BGP
- iBGP / eBGP
- AS-PATH
- LOCAL_PREF
- NEXT_HOP
- MED
- Route Reflector
By contrast, longer corporate or marketing-style words often break into multiple tokens.
In short: technical language is usually cheaper than buzzwords.
A Real BGP Troubleshooting Example
Here’s a common scenario:
“Traffic is exiting Miami instead of Tampa even though Tampa has higher bandwidth. Both sites run eBGP to different ISPs. Why?”
The prompt itself is relatively small, but the response could easily turn into a long explanation of BGP path selection, iBGP behavior, and enterprise routing design.
To keep the answer focused, add a constraint:
Explain why LOCAL_PREF overrides bandwidth in BGP exit selection. Limit to 200 tokens.
This usually produces a concise, technically accurate explanation without unnecessary background.
Token Limits vs Context Windows
Many people focus on token cost. In practice, engineers often run into context limits first.
A context window includes:
- Your current prompt
- Previous conversation history
- Hidden system instructions
- The model’s response
As conversations grow longer, older details can be dropped. That’s when responses start ignoring assumptions or repeating earlier questions.
This matters most during long-running efforts like migrations, design reviews, or extended troubleshooting.
Context Compression: A Practical Technique
One effective pattern is to intentionally compress context.
Instead of carrying thousands of tokens forward, ask for a concise summary you can reuse:
Summarize the BGP design decisions above in ≤200 tokens. This will be used as authoritative context.
You trade a large, messy history for a small, clean reference that keeps future conversations aligned.
Reusable Low-Token Prompt Examples
Explain BGP path selection order. Focus on LOCAL_PREF, AS_PATH, MED. ≤300 tokens.Root-cause asymmetric routing between two eBGP exits. Assume iBGP full mesh. ≤400 tokens.Review BGP design for dual-ISP redundancy. Call out risks only. ≤250 tokens.Analyze this BGP config for path-selection issues. Only list problems. ≤300 tokens.Provide BGP cutover checklist for ISP migration. Bullet points only. ≤200 tokens.
Simple constraints like these dramatically improve signal-to-noise.
Minor Nuances Worth Noting
A few small details are worth calling out to keep expectations grounded:
- Token counts can vary slightly between models and tokenizers, so any specific numbers should be treated as approximations rather than exact measurements.
- Specific token breakdowns depend on the tokenizer in use. For example, a word like “decisions” may be treated as one token in one model and split into multiple tokens in another.
- Context window sizes vary by model. Newer models support much larger context windows than older ones, which affects how much conversation history or documentation can be handled at once.
These differences generally don’t change how you should think about prompts, but they explain why exact token counts and limits can vary.
A Simple Rule to Remember
Tokens are burned by narrative, not by engineering.
- Protocol terms are cheap
- Deterministic logic is cheap
- Filler and repetition are expensive
If you treat AI like an engineering tool instead of a chatbot, you’ll get better answers with less overhead.
A Useful Default Header for Serious Work
When using AI for real engineering tasks, starting with a short directive helps:
You are assisting a senior network engineer.
Be concise and deterministic.
Avoid filler.
State assumptions explicitly.
Limit response to ≤400 tokens unless necessary.
This sets expectations immediately and keeps responses focused.
Closing Thoughts
Tokens aren’t just a billing concept. They’re also a way to control clarity, relevance, and continuity.
Once you understand how tokens work, you can:
- Ask better questions
- Get tighter answers
- Avoid context loss in long conversations
- Use AI effectively for network design, troubleshooting, and migrations
That’s the difference between using AI casually and using it as a professional engineering tool.