Lost in the Middle
Positional attention effects.
Lost in the Middle is a free AI Prompt Engineering lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Prompt Engineering learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The Lost-in-the-Middle Effect
Across long-context models, retrieval accuracy follows a U-shaped curve by position: facts at the beginning and end of the context are recalled well, while facts in the middle are recalled worst. This is the 'lost in the middle' effect.
- It is a property of attention and training, not random.
- It persists even when the answer is unambiguously present.
Position is not neutral; it is a reliability factor you must design around.
Why the Middle Suffers
Several pressures compound in the middle: positional biases favor recent and earliest tokens, training data over-represents 'answer near the start or end' patterns, and the sheer number of competing distractors in the interior dilutes attention to any single fact.
You cannot remove the bias from the model, but you can stop fighting it.
Place Critical Content at the Edges
The first and most reliable mitigation: put what the model must use at the boundaries. The task instruction and the highest-value evidence belong near the top and bottom, not buried in the center.
- Lead with the task and the key facts.
- Repeat the core instruction at the very end ('reminder of your task: ...').
context = (
TASK_INSTRUCTION +
KEY_EVIDENCE +
bulk_supporting_material +
TASK_REMINDER # restate at the tail
)Relevance-Ordered Placement
When you control the order of retrieved chunks, do not dump them in retrieval-score order front-to-back. Interleave so the most relevant chunks sit at both edges and the least relevant fill the middle — exploiting the U-curve deliberately.
This 'edge-loading' can recover a large fraction of middle-position loss.
def edge_load(chunks):
# chunks sorted by relevance desc
head, tail = [], []
for i, c in enumerate(chunks):
(head if i % 2 == 0 else tail).append(c)
return head + list(reversed(tail))Reduce the Haystack
The cheapest fix for middle loss is to have less middle. Aggressive pre-filtering — dropping irrelevant chunks before assembly — shrinks the interior where facts get lost and concentrates attention on what matters.
A 15k-token focused prompt has almost no vulnerable middle; a 600k-token prompt is mostly middle.
Signposting and Indexing
Give the model a map. A table of contents at the top plus clear section anchors lets the model navigate to relevant regions rather than relying on raw positional recall.
- Number sections and reference them in the question.
- Add explicit anchors like SECTION 7: PRICING the model can search for.
Structure partially substitutes for positional reliability.
# Top of prompt
# INDEX: [S1 Overview] [S2 Risks] [S3 Pricing] [S4 Appendix]
# Question: 'Using S3 only, what is the renewal fee?'Force a Scan Before Answering
Prompt the model to enumerate where relevant material appears before answering. The act of locating evidence first counteracts the tendency to skip the middle.
'First list every section that mentions the renewal fee with its anchor, then answer.' The explicit scan surfaces middle content that a direct question would miss.
ask = (
'Step 1: list all anchors mentioning the topic. '
'Step 2: quote the relevant line from each. '
'Step 3: answer using only those quotes.'
)Re-Ask With Repositioning
If a critical fact lands in the dead zone and recall is shaky, re-issue the call with that fact moved to an edge. For multi-fact tasks, you can run passes that rotate which facts occupy the strong positions and reconcile the results.
Position rotation is a practical robustness trick when a single layout is unreliable.
Chunk-and-Merge for Coverage
For exhaustive tasks ('find all violations') a single giant pass will miss middle items. Split into overlapping windows small enough to lack a weak middle, run each, then merge and dedupe results.
- Overlap prevents boundary misses.
- Merge step reconciles duplicates and conflicts.
Coverage tasks favor many small passes over one huge pass.
def coverage(doc, win, overlap):
out = []
for w in windows(doc, win, overlap):
out += extract(w)
return dedupe(out)Measure Your Own U-Curve
The severity of the effect varies by model and prompt shape. Characterize it: insert a canary at sweeping depths and plot recall. Use the curve to decide your safe context length and your edge-loading policy for that model.
Engineer against measured behavior, not folklore.
depths = [0.0, 0.25, 0.5, 0.75, 1.0]
recall = {d: probe_canary_at(d) for d in depths}
# Expect a dip around 0.5A Positional Robustness Playbook
To beat lost-in-the-middle: shrink the haystack, edge-load the most relevant content, restate the task at the tail, add an index and force a locate-before-answer scan, rotate positions on re-ask, and use chunk-and-merge for exhaustive coverage. Above all, measure the curve for your model and design to it.
Quick Check
You have a single must-use fact and a large body of supporting context, and you have noticed the model sometimes ignores the fact.
Recap: Positional Attention Effects
Long-context recall is U-shaped: edges are reliable, the middle is not. Counter it by shrinking the haystack, edge-loading the most relevant chunks, restating the task at the tail, adding an index and forcing a locate-before-answer scan, rotating positions on re-ask, and using chunk-and-merge for exhaustive coverage. Measure your model's actual curve and engineer placement to it rather than trusting uniform recall.
Frequently asked questions
Is the “Lost in the Middle” lesson free?
Yes — the full text of “Lost in the Middle” is free to read here on the web, and the AI Prompt Engineering course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Prompt Engineering course, upgrade to CoddyKit PRO.
What will I learn in “Lost in the Middle”?
Positional attention effects. You practise AI Prompt Engineering with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AI Prompt Engineering?
No prior experience is required. AI Prompt Engineering on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Lost in the Middle” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AI Prompt Engineering lesson?
Yes. Every AI Prompt Engineering lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.