How to Use DeFiLlama AI to Research Crypto Ecosystems

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Step-by-Step: How to Use DeFiLlama AI for Research

• Frame the right research question

This is where most beginners fail before they’ve typed a single prompt. Vague questions produce vague answers.

Bad: “Tell me about Sui.”

Good: “Is Sui’s DeFi ecosystem growing or contracting? What are the strongest signal protocols, and where is capital concentrating right now?”

Before touching LlamaAI, write your research question in one sentence. If you can’t, you’re not ready to research.

• Write a powerful, structured prompt

Prompting LlamaAI is different from casual chatting. The more structured your input, the more actionable your output. Use this template:

You are a senior crypto research analyst specialising in Layer 1 ecosystems, on-chain data, and early-stage opportunity discovery.

Your task is to conduct a deep, data-driven research report on [INSERT PROJECT / ECOSYSTEM / NARRATIVE].

The report must go beyond surface-level explanations and focus on extracting alpha, identifying growth signals, and uncovering asymmetric opportunities.

Structure your research as follows:

1. Executive Summary
- What is [PROJECT]?
- Key thesis: Why it matters right now
- One-paragraph investment or ecosystem insight

2. Core Architecture & Differentiation
- Explain the underlying technology and design
- Compare with key competitors or alternatives
- Strengths vs weaknesses

3. On-Chain Data Analysis (CRITICAL)
- TVL trends (growth/decline patterns)
- Volume trends (DEX, protocol, or relevant activity)
- Active users / addresses growth
- Liquidity flows (inflows/outflows)
- Developer activity (if available)

Highlight anomalies, sudden spikes, or early signals.

4. Ecosystem / Product Breakdown
Break down into key sectors relevant to the subject:
- (e.g., DeFi, NFTs, Gaming, Infrastructure, AI, etc.)

For each sector:
- List top projects
- Explain what they do
- Identify rising or underrated projects

5. Key Projects / Components Deep Dive
Select 5–10 important or high-potential elements within the ecosystem:

For each:
- Overview
- Traction metrics (users, volume, TVL, adoption)
- Unique edge
- Risks

6. Capital Flow & Narrative Analysis
- Where is liquidity or attention flowing?
- What narratives are forming?
- Are whales or smart money accumulating?

7. Risks & Weaknesses
- Technical risks
- Ecosystem or adoption risks
- Tokenomics or structural concerns

8. Alpha Section (MOST IMPORTANT)
- Undervalued projects or sectors
- Early trends before mainstream attention
- Opportunities for builders, investors, or airdrop farmers

9. Forward Outlook (3–12 months)
- Growth catalysts
- Potential adoption drivers
- What could drive success or failure

Requirements:
- Be analytical, not descriptive
- Avoid generic explanations
- Use data wherever possible
- Think like a hedge fund analyst looking for edge
- Highlight insights that are not obvious to the average user

Output format:
Clean, structured, and written like a professional research report.

After completing the report, include:
- 3 contrarian insights most people are missing
- 3 high-risk, high-reward bets
- A one-line “alpha thesis” summarizing the biggest opportunity

Ask for comparisons – “compare Sui to Solana on these metrics” forces context, not isolated numbers. Request the bear case – “what are the top 3 reasons this thesis fails?” is one of the most valuable prompts you can write. Chain your queries – start broad, then drill down: “now go deep on the top 3 lending protocols.”

• Extract insights, not just data

LlamaAI will hand you data. Your job is to find the meaning inside it. Three mental models that work:

• Identify alpha signals

Alpha is information the market hasn’t yet priced in. In DeFi, it usually appears in four forms:

1. Early traction – small TVL, triple-digit growth, no token yet
2. Beaten-down infrastructure – core protocols at 80%+ ATH discounts with institutional validation
3. Narrative convergence – two growing sectors pointing to the same protocol (e.g., AI + storage)

Case Study: Sui Ecosystem Deep Dive

Before we deep dive into the Sui ecosystem, here is the prompt I gave Llama AI for quality research:

You are a senior crypto research analyst specializing in Layer 1 ecosystems, on-chain data, and early-stage opportunity discovery.

Your task is to conduct a deep, data-driven research report on the Sui blockchain ecosystem.

The report must go beyond surface-level explanations and focus on extracting alpha, identifying growth signals, and uncovering asymmetric opportunities.

Structure your research as follows:

1. Executive Summary
- What is Sui?
- Key thesis: Why Sui matters right now
- One-paragraph investment or ecosystem insight

2. Core Architecture & Differentiation
- Explain Sui’s tech (object-based model, parallel execution, Move language)
- Compare with other L1s (Solana, Ethereum, Aptos)
- Strengths vs weaknesses

3. On-Chain Data Analysis (CRITICAL)
- TVL trends (growth/decline patterns)
- DEX volume trends
- Active addresses and user growth
- Stablecoin inflows/outflows
- Developer activity (if available)

Highlight anomalies, sudden spikes, or early signals.

4. Ecosystem Breakdown
Break down Sui’s ecosystem into key sectors:
- DeFi (DEXs, lending, derivatives)
- NFTs & Gaming
- Infrastructure (oracles, bridges, wallets)

For each sector:
- List top projects
- Explain what they do
- Identify rising or underrated projects

5. Key Projects Deep Dive
Select 5–10 important or high-potential projects on Sui:
For each:
- Overview
- Traction metrics (users, volume, TVL)
- Unique edge
- Risks

6. Capital Flow & Narrative Analysis
- Where is liquidity flowing within Sui?
- What narratives are forming? (e.g., gaming chain, high-speed DeFi, etc.)
- Are whales or smart money accumulating?

7. Risks & Weaknesses
- Technical risks
- Ecosystem risks
- Tokenomics or centralization concerns

8. Alpha Section (MOST IMPORTANT)
- Undervalued projects or sectors
- Early trends before mainstream attention
- Opportunities for builders, investors, or airdrop farmers

9. Forward Outlook (3–12 months)
- Growth catalysts
- Potential adoption drivers
- What could make Sui succeed or fail

Requirements:
- Be analytical, not descriptive
- Avoid generic explanations
- Use data wherever possible
- Think like a hedge fund analyst looking for edge
- Highlight insights that are not obvious to the average user

Output format:
Clean, structured, and written like a professional research report.

Let’s break down the result of this prompt:

Sui is a Layer 1 blockchain built by Mysten Labs, former Meta engineers who co-authored the Diem blockchain. What makes Sui architecturally distinct is that it doesn’t organise its ledger the way virtually every other blockchain does.

Instead of a sequential chain of account state changes, Sui stores its ledger as a Directed Acyclic Graph (DAG) of discrete objects. Every token, NFT, and game item is a unique object with explicit ownership. Transactions declare upfront which objects they’ll touch, letting the runtime execute non-conflicting transactions simultaneously without collision.

The result: sub-second finality, $0.0087 average transaction fee, and demonstrated throughput of 297,000 TPS.

The Current Snapshot

The Competitive Picture

The Fee Asymmetry Nobody Talks About

Sui’s 30-day chain fees are $178K. Its 30-day app fees are $2.27M, a 15x gap. This is structural: Sui deliberately prices gas cheaply (23.4% of all transactions are fully sponsored by dApps). The chain doesn’t extract rent from users. Applications do, and that’s where LPs and token holders capture value.

Key Insights: The Bullish, Bearish, and Hidden

Here is the full signal breakdown from this research:

🟢 Bullish Signals

• The only major L1 with positive 30-day TVL growth (+3.66%) in a period where Ethereum, Solana, and Avalanche are all declining by double digits is a clear relative strength divergence
• 3 US-listed spot ETFs are already trading (Greyscale GSUI, Canary SUIS, and 21Shares TSUI) with CME futures launching in May 2026. Institutional product infrastructure is live, not announced
• Whale positioning at 70–71% long with flat funding rates (no leverage squeeze risk) controlled accumulation without retail euphoria, the textbook early institutional pattern
• Stablecoin supply holding at $523M+, despite a 79% TVL drawdown. Dry powder is inside the ecosystem waiting, not exiting
• Developer growth of 159% vs Aptos’ 96%, 954 monthly active developers vs Aptos’ 465, winning the builder war by 2:1
• BTCfi is now 10%+ of TVL (~$200M+ in BTC-backed assets). Bitcoin holders are willingly bridging for Sui DeFi yield, a strong institutional conviction signal
• NAVI (+7.05%), Ondo (+45.75%), Haedal (+9.62%) posting positive 30d growth, with selective, intelligent capital rotation into quality protocols

🔴 Bearish Signals

• $232M+ in security exploits including the $223M Cetus hack (May 2025), the worst single DeFi hack in Sui’s history, with three additional protocol-level exploits since
• 79% drawdown from $2.64B Peak, even accounting for token price compression, real capital outflow has occurred
• P/S ratio of 208,738x on-chain fees are priced for extraordinary future growth; if growth disappoints, there’s significant downside
• 113–127 validators vs Ethereum’s 900,000+ validator set concentration creates censorship risk, as demonstrated by the post-Cetus transaction freeze
• ~495M SUI tokens unlocking in next 12 months (~$478M at current price) 4.95% annual supply inflation as a headwind
• McapTVL ratio of 6.7x, paying a significant premium for the chain asset relative to locked value
• DeepBook (DEEP) trading 89.84% below ATH with 24.91% mcap/FDV heavy dilution ahead from unreleased token supply

🔍 Hidden Alpha

• Ember Protocol: $51.8M TVL, $359K/month fees, NO TOKEN the highest-conviction airdrop opportunity in the ecosystem; real economic activity without a token means the team is building, not farming
• The stablecoin-to-TVL ratio near parity (93%); historically, this compression precedes explosive TVL growth when risk appetite returns to a mean-reversion setup
• DEEP at 15.67% APY with near-zero volatility (CV: 0.014) is the best risk-adjusted yield on Sui while holding a “picks and shovels” infrastructure position
• Walrus (WAL) already surpassed Arweave in stored data (467TB vs 385TB) in its first year under ownership by AI narrative traders who haven’t discovered it yet
• Zo Protocol showing +467% 90-day growth from a real base, the single highest growth rate of any Sui protocol above $5M TVL, and an early-stage breakout pattern
• Ondo Finance on Sui: +45.75% monthly, +43.6% 90-day strongest sustained growth trajectory in the ecosystem; RWA sector has only 4 protocols and $58M TVL on a chain with institutional ETF infrastructure

CONCLUSION

Every insight in this article came from asking systematic questions of on-chain data. The methodology works identically on Solana, Cosmos, Base, Avalanche, or any ecosystem with DeFiLlama coverage.

The formula is always: Relative strength + Real fees + Tokenless traction + Capital flow signals = Alpha before narrative.

DeFiLlama AI doesn’t guarantee returns. No tool does. But it gives you the kind of data infrastructure that, used correctly, systematically shifts the odds in your favour. In a market where most people are guessing based on vibes, that edge compounds.

With the good prompt, DeFiLlama AI is a powerful tool

Start with one question. Build the habit. The research will follow.