RAG Architect's Studio — Context Budget & Pattern Matcher

Context Budget Planner

Your context window is real estate. Toggle each layer to see how your budget fills — and where risks appear. Every token you include competes for the model's finite attention.

Context used 24%

0%100% (128K tokens)

Toggle layers on the left to fill your context window.

Attention Map — Lost in the Middle

My field notes

In college, we all knew someone who'd fill half the answer sheet with nonsense just to look thorough. Strong start, strong ending, pure filler in the middle. LLMs have the same blind spot — strong attention at the start and end of context, the middle fades. There's a published research paper on this (Lost in the Middle, Liu et al., 2023). The slider below shows how that degradation looks as your context fills up.

LLMs pay strong attention to what's at the start and end of the context. The middle fades. Drag the slider to see attention erode as context fills.

Context fill: 20% of window used

Near emptyNearly full

START

95%

attention retained

System prompt, few-shot examples

MIDDLE

85%

attention retained

Drops as context grows ↓

END

90%

attention retained

User query always goes here

The fix: Put your most critical context at the start or end. Re-rank chunks so the highest-relevance chunk sits at position 1 (not buried in the middle). Use a needle-in-haystack test to verify your model actually finds it.

Signal vs Noise Lab — 3 Compression Methods

Think of a college group chat after exam results — 80% noise, 20% signal. A retrieved chunk has the same problem: the fact you need is buried in filler. Pick your filter below and see what survives — and what the model never needed to read.

Original chunk

180 tokens

Source: College group chat — after Semester 4 results

Extract — when?

Chunk has a few golden sentences buried in filler. You need precision over brevity. No paraphrasing risk.

Summarize — when?

Long chunk, qualitative content. Losing some nuance is acceptable. Best for narrative passages.

Compress — when?

Many chunks, speed matters. Drop stop-words and filler. Preserves almost all facts telegraphically.

RAG Pattern Matcher

Answer 3 questions about your problem. The matcher recommends the right RAG pattern and shows you exactly what you're trading away.

① Are your data entities interconnected — people, companies, products that reference each other?

② Does a single query often span multiple data sources or require multi-step reasoning?

③ Is latency critical — does the answer need to return in under 2 seconds?

The Autonomy Dial

Every step toward agentic RAG gives you more intelligence but demands more in latency, cost, and failure management. Drag the dial and watch the trade-offs shift live.

← Deterministic / Naive Runaway Agent →

Naive RAG

Latency

~100ms

Cost / query

$0.001

Accuracy ceiling

60%

Failure modes

1

Graph RAG Lens — Flat vs Rich Retrieval

Vector search finds semantically similar chunks. Graph RAG finds chunks AND traverses the relationships around them. Same query — radically different context depth. Toggle to compare.

Graph structure

Context passed to LLM

Use Graph RAG when you can answer YES to all three:

✓Your data has named entities that connect to each other (people, companies, products)

✓A query answer requires traversing 2+ hops across relationships

✓You can tolerate the upfront cost of defining a graph schema (Neo4j/Cypher)

Eval Clinic — Diagnose Your RAG

Every RAG failure lives in one of four quadrants. Click your quadrant — or pick a symptom — to get a diagnosis, the metrics that catch it, and the fix.

Quick symptom check — what are you seeing?

Generation Quality: Low

Generation Quality: High

Retrieval: ↑ High

🧩

Good chunks, bad answer

LLM ignoring context

✓ TARGET

🎯

Healthy RAG

Both stages working

Retrieval: ↓ Low

🔥

Both broken

Fix retrieval first

⚠️ DANGER

👻

Hallucinating confidently

Most dangerous quadrant